Abstract The vehicle routing problem with time windows (VRPTW) has gained much attention recently due to its wide application in operations research and logistics. VRPTW has been proven to be an NP-hard problem whose optimal solution is computationally costly. Scholars have proposed many methods, such as exact algorithms, heuristics, and metaheuristics, to find near-optimal solutions for the VRPTW. Exact algorithms are limited to small-scale problems, while heuristic algorithms and metaheuristics often converge to locally optimal solutions, despite their applicability to larger-scale problems. This paper proposes a novel membrane-inspired evolutionary algorithm framework (MEAF) consisting of isolated evolutionary rules, communication output rules, communication input rules, fusion-exchange information operation, and membrane dissolution rules. By leveraging the advantages of multiple metaheuristics algorithms and avoiding the pitfalls of local optima, MEAF offers a promising solution to address complex problems. The effectiveness of the proposed MEAF is verified by applying three classical metaheuristics, namely Genetic Algorithm (GA), Ant Colony System (ACS), and Particle Swarm Algorithm (PSO), to solve the VRPTW problem. The experiments are run on 56 instances of Solomon with 100 client benchmarks. The evaluation of the experimental results combined with the mean and standard deviation values show that the algorithm performs better in 54 out of 56 instances, demonstrating the effectiveness and stability of the proposed algorithm.

Intuitionistic fuzzy sets extend traditional fuzzy sets by incorporating degrees of membership, non-membership, and indeterminacy, making them particularly useful in contexts where uncertainty and hesitancy are prevalent. Rough soft sets combine rough sets' approximation capabilities with soft sets' flexible, parameterized approach to managing uncertainty. This study introduces Intuitionistic Fuzzy Rough Soft (IFRS) sets, integrating these advantages to create a robust framework for handling uncertainty, vagueness, and ambiguity in complex decision-making environments. The paper meticulously defines operations, operators, and measures between IFRS sets, establishing their characteristic properties through rigorous mathematical demonstrations. An innovative algorithm is proposed to address multi-criteria decision-making problems within this framework. The algorithm's effectiveness is thoroughly evaluated through comparisons with state-of-the-art algorithms using reputable datasets in medical consultation, agricultural land evaluation, educational support, and sensitivity analysis experiments. The results demonstrate the proposed algorithm's superior performance and robustness in complex decision-making scenarios, highlighting its potential as a valuable practical tool.

High utility itemset mining (HUIM) is one of the most compelling problems in data mining, extending frequent itemset mining (FIM) and serving as a crucial method for analyzing customer behavior. Many HUIM algorithms have been proposed to improve execution time and memory consumption. However, most assume that the profit is fixed for each item in a database, which is unrealistic. Some algorithms address products with unstable transaction profits but still need to run faster due to ineffective pruning strategies. Additionally, generalizing items into categories is often neglected. To address these issues, this paper considers a more practical database type that integrates unstable profits with a taxonomy of items. The proposed algorithm, CLHUN (Cross-level High Utility Itemset Mining in a Database with Unstable and Negative Profits), combines efficient techniques such as item sorting and tighter upper bounds to prune the search space. Furthermore, it introduces strategies to eliminate unpromising items during mining and reduce the number of transaction scans. Several experiments were conducted to evaluate the algorithm's performance. Results demonstrate that CLHUN is efficient with these techniques and strategies.

For many years, topological data analysis (TDA) and deep learning (DL) have been considered separate data analysis and representation learning approaches, which have nothing in common. The root cause of this challenge comes from the difficulties in building, extracting, and integrating TDA constructs, such as barcodes or persistent diagrams, within deep neural network architectures. Therefore, the powers of these two approaches are still on their islands and have not yet combined to form more powerful tools for dealing with multiple complex data analysis tasks. Fortunately, we have witnessed several remarkable attempts to integrate DL-based architectures with topological learning paradigms in recent years. These topology-driven DL techniques have notably improved data-driven analysis and mining problems, especially within graph datasets. Recently, graph neural networks (GNNs) have emerged as a popular deep neural architecture, demonstrating significant performance in various graph-based analysis and learning problems. Explicitly, within the manifold paradigm, the graph is naturally considered as a topological object (e.g., the topological properties of the given graph can be represented by the edge weights). Therefore, integrating TDA and GNN is considered an excellent combination. Many well-known studies have recently presented the effectiveness of TDA-assisted GNN-based architectures in dealing with complex graph-based data representation analysis and learning problems. Motivated by the successes of recent research, we present systematic literature about this nascent and promising research direction in this article, which includes general taxonomy, preliminaries, and recently proposed state-of-the-art topology-driven GNN models and perspectives.

Pattern mining, one of the data analysis approaches, provides meaningful assistance for various business services, such as product recommendation and marketing. However, certain real-world data contain uncertain characteristics, and some business services want to consider the uncertainty of data. Uncertain pattern mining is an advanced technique for discovering more useful patterns from uncertainty-driven data with uncertain information about items. However, although many business services create and process incremental data in real-time, most of the previous uncertain pattern mining techniques have limitations in analyzing incremental data since they mainly focus on processing static data. To address the limitations, we present a list-based uncertain pattern mining method that effectively analyzes incremental uncertainty-driven data in real time by scanning stream data only once. In addition, uncertainty-driven data analytics can be executed efficiently due to the list structure that is effective in construction and mining. The tests of performance for runtime, memory consumption, and scalability are performed using real datasets and synthetic datasets, which illustrate that the suggested technique reveals outstanding performance compared to state-of-the-art algorithms. The additional case study evaluations with concept-drifting tests as well as accuracy and significance tests demonstrate the practical applications of the algorithm and the quality of the extracted results.

As the number of scientific publications continues to increase at a dizzying rate, researchers face challenges related to spending too much time and effort searching for appropriate papers to cite in their work. Citation recommendation models have thus been developed to automatically generate a list of relevant papers for a specific text passage, thus helping to reduce the workload for scientists and contribute to better-quality research. Consequently, this research direction has recently attracted significant interest in the scientific community. However, the current citation recommendation models still focus primarily on the citation context and do not adequately address the metadata of papers, such as the citation links, publication time, and venue. To overcome these problems, in this study, we propose the SciBERT-GraphSAGE which is a hybrid deep learning-based model for recommending a list of academic papers by considering both the citation context and this article’s metadata. Our model has two important components: 1) SciBERT for text data representation learning and 2) GraphSAGE for learning the representations of this article’s citation links. We validate the effectiveness of our model on three benchmark datasets: 1) FullTextPeerRead; 2) ACL; and 3) RefSeer. The results from experiments demonstrate that our novel SciBERT-GraphSAGE model outperforms previous advanced models in terms of Recall@K, mean reciprocal rank (MRR), and mean average precision (MAP).

In a manufacturing factory, product lines are organized by several constituents and exhibit a profit value, i.e., income from products. Erasable patterns are less profitable patterns whose gain, i.e., the sum of product profits, does not exceed a user-defined threshold. Mining erasable patterns provides the necessary information to users who want to increase profits by erasing less profitable patterns. There are requirements for a method which efficiently manages uncertain databases in incremental environments to identify erasable patterns that consider uncertainty. Because our novel technique uses a list structure, it is more efficient at finding erasable patterns from incremental databases. Moreover, accumulated stream data should be handled efficiently to identify new useful patterns in both additional data and the existing data. In this article, an algorithm using a list-based structure is proposed to extract erasable patterns containing valuable knowledge from uncertain databases in real time with effective and productive performance. In order to derive erasable patterns from continuously accumulated stream databases, the structure efficiently manages the information gathered from the previous database. Extensive performance and pattern quality evaluations were conducted using real and synthetic datasets. The results show that the algorithm performs up to seven times faster than state-of-the-art erasable pattern mining algorithms on real datasets and scales adeptly on synthetic datasets while delivering reliable and significant result patterns.

Knowledge mining from single graph plays an important role in decision support systems on single graphs such as social networks, bioinformatics, etc. In recent years, the problem of Frequent Subgraph Mining (FSM) from a single graph have been developed and attracted several studies. However, the problem of mining association rules or links from frequent subgraphs has not had many contributions. In this article, we state the problem of direct mining association rules from frequent subgraphs. Existing approaches on this topic perform the task in two phases. First, they traverse the search space to directly discover parent-child relationships from the discovered frequent subgraphs, then association rules are generated. We propose a one-phase algorithm, named So-GPARs, to generate rules as soon as frequent supergraphs are constructed from already existing frequent subgraphs. Our experiments on three single graph datasets show that the one-phase algorithm is more efficient than the two-phase algorithm in terms runtime of the rules generating phase.

Many researchers have been investigating and applying a new trend of data mining , namely high occupancy itemset mining. Frequent itemset mining often returns a large set of itemsets, but businesses need a smaller set of inputs to investigate or send into a recommendation system to quickly make decisions. Applying an occupancy measure to a support-based mining framework will thus bring many benefits for decision support systems , while managers will benefit by having a new method to visualize reports and analyze data more efficiently. Similar to frequent itemset mining, mining high occupancy itemsets can be applied on any transaction database. In this research, we apply additional conditions to eliminate unqualified itemsets and integrate the property of equivalence class to reduce the runtime of the k-itemsets generation process. Moreover, a new theorem is stated and applied to a specific class of databases so that it is not necessary to calculate the upper-bound occupancy, and this speeds up the process as well as reduces memory requirements with regard to generating high occupancy itemsets. We develop two new algorithms, fast high occupancy itemset mining (FHOI) and depth first search (DFS) for high occupancy itemset mining (DFHOI) to solve the problem. Our new algorithms are examined experimentally using different databases to evaluate its performance in term of runtime and memory usage.

Abstract In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

Recent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in the context of HIN. In order to address these challenges, in this article, we propose a novel approach of semantic-aware HIN embedding-based recommendation, called SemHE4Rec. In our proposed SemHE4Rec model, we define two embedding techniques for efficiently learning the representations of both users and items in the context of HIN. These rich-structural user and item representations are then used to facilitate the matrix factorization (MF) process. The first embedding technique is a traditional co-occurrence representation learning (CoRL) approach which aims to learn the co-occurrence of structural features of users and items. These structural features are represented for their interconnections in terms of meta-paths. In order to do that, we adopt the well-known meta-path-based random walk strategy and heterogeneous Skip-gram architecture. The second embedding approach is a semantic-aware representation learning (SRL) method. The SRL embedding technique is designed to focus on capturing the unstructured semantic relations between users and item content for the recommendation task. Finally, all the learned representations of users and items are then jointly combined and optimized while integrating with the extended MF for the recommendation task. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed SemHE4Rec in comparison with the recent state-of-the-art HIN embedding-based recommendation techniques, and reveal that the joint text-based and co-occurrence-based representation learning can help to improve the recommendation performance.

Fuzzy utility (FU) pattern mining with an advantage in human reasoning has become one of the interesting topics in studies of knowledge discovery. The discovered information in FU pattern mining from real-life quantitative databases with item profits is suitable for interpreting data from a human perspective because it is not expressed using numerical values but linguistic terms which consist of natural languages. State-of-the-art approaches in this literature provide extended results by considering temporal factors, such as seasons, which can be influential in real-life situations. However, they still suffer from scalability issues because they are based on level-wise approaches which generate a number of candidates. In this article, we propose a scalable and efficient approach with a novel data structure for mining high temporal FU patterns without generating candidates. Efficient pruning techniques and algorithms are presented to improve the performance of the proposed approach. Performance experiments on both real and synthetic datasets show that the suggested algorithm has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability.

Sequential pattern mining in general and one particular form, clickstream pattern mining, are data mining topics that have recently attracted attention due to their potential applications of discovering useful patterns. However, in order to provide them as real-world service applications, one issue that needs to be addressed is that traditional algorithms often view databases as static. In reality, databases often grow over time and invalidate parts of the previous results after updates, forcing the algorithms to rerun from scratch on the updated databases to obtain updated frequent patterns. This can be inefficient as a service application due to the cost in terms of resources, and the returning of results to users can take longer when the databases get bigger. The response time can be shortened if the algorithms update the results based on incremental changes in databases. Thus, we propose PF-CUP (pre-frequent clickstream mining using pseudo-IDList), an approach towards incremental clickstream pattern mining as a service. The algorithm is based on the pre-large concept to maintain and update results and a data structure called a pre-frequent hash table to maintain the information about patterns. The experiments completed on different databases show that the proposed algorithm is efficient in incremental clickstream pattern mining.

Recent advances in deep learning have tremendously leveraged the performance of network representation learning (NRL). Multiple deep learning-based NRL models have been proposed recently to effectively handling primitive tasks of information network analysis and mining (INAM) domain, including link prediction (LP). LP is considered as an important one due to its multiple applications in many disciplines. In the recent few years, LP in dynamic networks has attracted a lot of attention from researchers to propose novel algorithms for better capturing both rich structural and evolutional information of complex information networks (INs). However, recent models are mainly concentrated on preserving the sequential representations of a given network over time. They have largely ignored other important structural features, such as: intracommunity which contributes to the creation of links between network nodes. In this article, we propose a novel community-driven dynamic NRL technique upon the RNN+GCN framework, called: ComGCN. Specifically, the ComGCN model is a combination of microscopic (node embedding-based) and mesoscopic (intracommunity-based) dynamic network embedding approach which enable effectively handling the LP problem in context of dynamism. Extensive experiments on real-world dynamic networks demonstrated the effectiveness of the proposed model compared with recent state-of-the-art baselines.

Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added into each node element in both the SPPC-tree and C-Lists to quickly prune nonclosed clickstream. Experimental results conducted on several datasets show that the proposed method is better than the previous techniques and improves the runtime and memory usage in most cases, especially when using low minimum support thresholds on the huge databases.

The Internet of Things (IoT) environment includes things that exchange information using data generated through sensors. The IoT technology can be executed efficiently by providing the necessary information to things instead of transmitting all the data. There is a need for an algorithm that can extract meaningful information for the things from the data. High utility pattern mining, which can handle the characteristics of real-world databases better than traditional pattern mining methods, has been actively researched. The traditional high utility pattern mining techniques find meaningful patterns from static databases. Therefore, these techniques are not suitable for the dynamically changing databases in the real world. In order to solve this problem, a variety of methods that consider the modifications, deletions, and insertions of transactions are proposed. The prelarge concept, which can be efficiently operated by reducing the rescanning of the original database using two thresholds, was also proposed in order to handle the limitations of the methods that are used for static databases. In the prelarge concept, the large patterns and the prelarge patterns that are discovered are maintained and used for the next transaction change. In this article, we proposed a new method pre-large based incremental high utility pattern mining for transaction modification (PIHUP-MOD) implemented as a tree structure in order to handle the transaction modifications. The method has an efficient structure and techniques to mine high utility patterns in the transaction modifications with the prelarge concept. We conducted the performance evaluation, and the experimental results on the real datasets and the synthetic datasets showed that the proposed approach has a better performance than the state-of-the-art approaches in terms of the runtime, memory usage, and scalability.

Topological data analysis is a new theoretical trend using topological techniques to mine data. This approach helps determine topological data structures. It focuses on investigating the global shape of data rather than on local information of high-dimensional data. The Mapper algorithm is considered as a sound representative approach in this area. It is used to cluster and identify concise and meaningful global topological data structures that are out of reach for many other clustering methods. In this article, we propose a new method called the Shape Fuzzy C-Means (SFCM) algorithm, which is constructed based on the Fuzzy C-Means algorithm with particular features of the Mapper algorithm. The SFCM algorithm can not only exhibit the same clustering ability as the Fuzzy C-Means but also reveal some relationships through visualizing the global shape of data supplied by the Mapper. We present a formal proof and include experiments to confirm our claims. The performance of the enhanced algorithm is demonstrated through a comparative analysis involving the original algorithm, Mapper, and the other fuzzy set based improved algorithm, F-Mapper, for synthetic and real-world data. The comparison is conducted with respect to output visualization in the topological sense and clustering stability.

Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above. Therefore, knowledge-based software about how to find meaningful information efficiently by analyzing databases with these characteristics is required. In this article, we propose an intelligent information system that calculates the influence of the insertion time of each batch in a large-scale stream database by applying the sliding window model and mines recent high utility patterns without generating candidate patterns. In addition, a novel list-based data structure is suggested for a fast and efficient management of the time-sensitive stream databases. Moreover, our technique is compared with state-of-the-art algorithms through various experiments using real datasets and synthetic datasets. The experimental results show that our approach outperforms the previously proposed methods in terms of runtime, memory usage, and scalability.

To overcome the limitation of high-utility itemset mining, more compact, lossless, and concise representations of high utility itemsets (HUIs) have been proposed in previous works, such as closed HUIs (CHUIs) or maximal HUIs (MHUIs). Focusing into MHUI mining, in this article, we present efficient approaches to directly mine MHUIs from transactional databases without generating any candidates. The proposed algorithms, which all execute in one phase, utilize many efficient data structures and pruning techniques such as EUCP combined with EUCS, CUIP combined with FUCS, and the P-set structure to significantly reduce the search space and remove nonpromising itemsets, thus, increase the performance of the MHUI mining process. Furthermore, while previous works assumed that the unit profit of items is fixed, which is not practical in many real-world applications, our work resolved this issue by applying a new utility calculation into the mining process to reflect the true nature of real-world databases, thus, generating more accurate results.

To date, there remains a lack of works that focus on the problem of mining clickstream patterns. Although it is an alternative to use the general algorithms for sequential pattern mining to mine clickstreams, their performance may suffer and the resources needed are more than necessary. In this paper, we present a novel data structure, called index-IDList, that is suitable for clickstream pattern mining. Based on this data structure, we present a vertical format algorithm named CUI (Clickstream pattern mining Using Index-IDList). The experiments are carried out on four real-life clickstream databases and the results show that our proposed method is effective and efficient in terms of runtimes and memory consumption.

One of the key tasks of data mining is frequent pattern mining (FPM), discovering patterns that frequently occur in transactions of transaction databases. A majority of methods of FPM only mine patterns in the same transactions, they do not consider patterns across several transactions (intertransaction patterns- ITPs) in a transaction database. So far, a number of algorithms have been proposed to mine ITPs. However, they still have many problems that need improving to make them more efficient in mining time and memory usage. This paper presents an efficient method of mining frequent closed inter-transaction patterns (FCITP) from a transaction database. Our proposed method generates inter-transaction patterns at the 1-pattern level. Besides, we propose an efficient pruning strategic to prune infrequent inter-transaction 1- patterns and quickly generate frequent inter-transaction 2- patterns. To eliminate non-closed patterns, the proposed method adopts properties of frequent closed patterns and pruning strategies. In addition, a Diffset-based strategy for the efficient computation of the supports of closed inter-transaction patterns is described. Experiment results conducted on several datasets of various characteristics show that our algorithm outperforms the state-of-the-art algorithms in terms of mining time and memory usage in most cases.

Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time in gathering up-to-date erasable itemsets. In this paper, we propose the concept of the ε-quasi-erasable itemsets and use it to improve mining performance. The itemsets in both the original database and the new product can then be divided into erasable, ε-quasi-erasable, and nonerasable. Thus, there are nine combinations that are then processed in different ways. Experiments are finally made to verify the performance.

Erasable-itemset (EI) mining is to find the itemsets that can be eliminated but do not greatly affect the factory's profit. In this paper, an incremental mining algorithm for erasable itemset is proposed. It is based on the concept of the fast-update (FUP) approach, which was originally designed for association mining. Experimental results show that the proposed algorithm executes faster than the batch approach in the intermittent data environment.

Text clustering is an important topic in text mining. One of the most effective methods for text clustering is an approach based on frequent itemsets (FIs), and thus, there are many related algorithms that aim to improve the accuracy of text clustering. However, these do not focus on the weights of terms in documents, even though the frequency of each term in each document has a great impact on the results. In this work, we propose a new method for text clustering based on frequent weighted utility itemsets (FWUI). First, we calculate the Term Frequency (TF) for each term in documents to create a weight matrix for all documents. The weights of terms in documents are based on the Inverse Document Frequency. Next, we use the Modification Weighted Itemset Tidset (MWIT)-FWUI algorithm for mining FWUI from a number matrix and the weights of terms in documents. Finally, based on frequent utility itemsets, we cluster documents using the MC (Maximum Capturing) algorithm. The proposed method has been evaluated on three data sets consisting of 1,600 documents covering 16 topics. The experimental results show that our method, using FWUI, improves the accuracy of the text clustering compared to methods using FIs.

In this paper, we propose the WUN-set (Weighted Utility Nodeset) structure, an extension of the Nodeset structure, to solve the problem of mining frequent weighted utility itemsets from a quantitative database. Firstly, some theorems are developed to compute quickly the weighted utility support of an itemset. An algorithm is then proposed for the fast mining frequent weighted utility itemsets. The experimental results on both sparse and dense databases show that the proposed method outperforms existing methods.

Sohrabi and Barforoush proposed the BVBUC (Bitwise vertical bottom up colossal) algorithm for mining colossal patterns based on a bottom up scheme. It, however, spends more time to check subsets and supersets, because it generates a lot of candidates and consumes more memory usage to store these. In this paper, we propose a new method for mining colossal patterns. Firstly, the CP (Colossal Pattern)-tree is designed. Next, we develop two theorems to fast compute patterns of nodes and prune nodes. Based on the CP-tree and these theorems, an algorithm (named CP-Miner) is proposed to solve the problem of mining colossal patterns. Experimental results show that CP-Miner is more efficient than BVBUC in both the mining time and search space.

Mining frequent weighted item sets (FWIs) from weighted items transaction databases (WITDs) has taken the interest of many researchers and there have been several works related to mining FWIs in recent years. Beside, in real world applications, sparse weighted items transaction databases (SWITDs) are very popular. For example, in the super market there are many items, but in the transaction there is only a small number of items. This paper proposes an interval word segment (IWS) structure to store and process tidsets for enhancing effectiveness of mining FWIs from SWITDs. With this structure, intersection operations of tidsets between two item sets are performed blazingly fast. Experimental results obtained on a number of spare databases show that IWS outperforms the existing methods.

Frequent pattern mining generates a lot of candidates which spends a lot of usage memory and mining time. Besides, in real applications, a small number of frequent patterns are used. Therefore, the problem of mining top-rank-k frequent patterns (TRFPs) is an interesting topic in recent years. This paper proposes iNTK algorithm for mining TRFPs. This algorithm employs N-list structure generated by PPC-tree to reduce the memory usage. Besides, the subsume concept is also used to enhance the process of mining TRFPs. The experimental results show that iNTK outperforms NTK in terms of mining time and memory usage.

This paper proposes an algorithm to hide quickly high utility sensitive itemsets. To reduce the execution time, when an item from the sensitive itemset is selected, the rated value is calculated from each transaction containing this item and all values which need be hidden are modified at the same time. Experimental results show that the proposed algorithm improves significantly the runtime.

This paper proposes an improved version of the MERIT algorithm, deMERIT+, for mining all "erasable item sets". We first establish an algorithm MERIT+, a revised version of MERIT, which is then used as the foundation for deMERIT+. The proposed algorithm uses: a weight index, a hash table and the "difference" of Node Code Sets (dNC-Sets) to improve the mining time. A theorem is derived first to show that dNC-Sets can be used for mining erasable item sets. The experimental results show that deMERIT+ is more effective than MERIT+ in terms of the runtime.

Frequent item set mining is a fundamental element with respect to many data mining problems. Recently, the PrePost algorithm has been proposed, a new algorithm for mining frequent item sets based on the idea of N-lists. PrePost in most cases outperforms other current state-of-the-art algorithms. In this paper, we present an improved version of PrePost that uses a hash table to enhance the process of creating the N-lists associated with 1-itemsets and an improved N-list intersection algorithm. Furthermore, two new theorems are proposed for determining the "subsume index" of frequent 1-itemsets based on the N-list concept. The experimental results show that the performance of the proposed algorithm improves on that of PrePost.

The utility based itemset mining approach has been discussed widely in recent years. There are many algorithms mining high utility itemsets by pruning candidates based on estimated utility values, and based on transaction-weighted utilization values. These algorithms aim to reduce search space. Besides, candidate pruning based on transaction-weighted utilization value is better than other strategies. In this paper, we propose TWU-Mining, a novel algorithm based-on WIT-tree for improving the cost of time and search space. Experiments show that the proposed algorithm is more effective on the testing databases.