Frequent pattern mining has gained popularity in the realm of knowledge discovery and big data analytics as it identifies sets of items that frequently co-occur (e.g., popular merchandise items or social events). In general, frequent pattern mining can be broadly classified into two categories: (i) transaction-centric algorithms that mines frequent patterns horizontally and (ii) item-centric mining algorithms that mines frequent patterns vertically. Irrespective of their categories, traditional frequent pattern mining algorithms aim to find Boolean frequent patterns, revealing whether some specific items are present in (or absent from) the discovered patterns. In the context of social network mining and analytics, Boolean frequent pattern algorithms can help reveal whether a social entity follows another in a network or on a social networking site. However, in numerous real-life applications, quantities of items within patterns become essential. For example, the quantity of followed items (e.g., like posts) can significantly influence the social interactions between entities in a network. In this paper, we present a social network mining and analytics algorithm---called QSN---for discovering quantitative frequent patterns from social networks. The algorithm represents the big data as a collection of item-centric bitmaps, each capturing the absence or presence of a transaction containing the item, along with the quantity of that item in each transaction. Subsequently, it vertically mines quantitative frequent patterns, strategically avoiding the generation of an excessive number of redundant candidate patterns, thereby accelerating the mining process. Results of our evaluation demonstrate the superiority of our QSN algorithm over the existing horizontal quantitative frequent pattern algorithm called MQA-M, highlighting its efficacy in social network mining and analytics.

Frequent pattern mining has been extended to the mining of other useful patterns. These include high-utility patterns. Many traditional high-utility mining algorithms focus on algorithmic efficiency when mining high-utility patterns from static databases. These algorithms rely on an assumption that the unit utility for a given item is a constant. However, as we are living in dynamic world where the unit utility (external unit profit) may change over time, such an assumption may not truly reflect reality in the real world. However, to the best of our knowledge, not a lot of works were done on mining dynamic profit from data streams yet. The emergence of big data has led to some performance challenges such that proper big data management techniques are needed for knowledge discovery from dynamic data streams. Traditional static data mining algorithms cannot directly apply to dynamic data. Furthermore, information in the data stream might not be uniformly distributed so it introduces extra challenges to process the data. Using big data stream processing platforms is necessary when mining real-world data stream. Leveraging the big data processing framework requires having scalable algorithms. In this paper, we present an enhanced high-utility data stream algorithm—called EHUI-Stream—to speed up the execution time and reduce memory usage. Utilizing our proposed algorithm, the data stream mining performance is expected to be further enhanced against both real-world datasets and synthetic datasets. Evaluation results on real-life data demonstrate the effectiveness of our platform in scalable high-utility pattern mining for dynamic profit from data streams for social and behavioral analytics.

Frequent itemset (or frequent pattern) mining is a technique used in big data mining to discover frequently occurring sets of items (such as popular co-purchased merchandise) and has numerous applications in the field of databases. Traditional frequent pattern mining algorithms only look at Boolean mining; that is, considering only the presence or absence of an item in an itemset. In this paper, we present an algorithm for mining interesting quantitative frequent patterns. Our qEclat (or Q-Eclat) algorithm extends the common Eclat algorithm to be able to vertically mine quantitative patterns. When compared with the existing MQA-M algorithm (which was built for quantitative horizontal frequent pattern mining), our evaluation results show that qEclat mines quantitative frequent patterns faster.

Frequent pattern mining is a popular technique in big data mining and analytics. It discovers frequently occurring sets of items (e.g., popular merchandise items, frequently co-occurring events) from big data found in numerous database engineered applications. These frequent patterns can be discovered horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of their mining direction (horizontal or vertical), traditional frequent pattern mining algorithms aim to discover Boolean frequent patterns in the sense that patterns capture the presence (or absence) of items within the discovered patterns. However, there are many real-life situations, in which quantities of items within the patterns are important. For example, the quantity of items may also affect profits of selling the items within the discovered patterns. Hence, in this paper, we present an algorithm for vertical mining of interesting quantitative frequent patterns. This Q-Eclat algorithm first represents the big data as a collection of equivalence classes according to their prefix item labels. Each domain item is represented by one of these classes. Their corresponding item-centric sets capture (a) IDs of transactions containing the item, as well as (b) the quantity of that item in each transaction. With this representation, our algorithm then vertically mines quantitative frequent patterns. When compared the existing MQA-M algorithm (which was built for quantitative horizontal frequent pattern mining), evaluation results show that our quantitative vertical Q-Eclat algorithm takes shorter runtime to mine quantitative frequent patterns.

Machine learning, as a tool, has become critical for decision-making mechanisms in the modern world. It has applications in a wide range of areas, including finance, healthcare, justice, and transportation. Unfortunately, machine learning is often considered as a “black box”. As such, recommendations made by machine learning techniques, as well as the reasoning behind those recommendations, are not easily understood by humans. In this paper, we present an explainable artificial intelligence (XAI) solution that integrates and enhances state-of-the-art techniques to produce understandable and practical explanations to end-users. To evaluate the effectiveness of our XAI solution for data science, we conduct a case study on applying our solution to explaining a random forest-based predictive model on customer churn. Results show the practicality and usefulness of our XAI solution in practical applications such as data science on customer churn.

In the current era of big data, advanced technology has led to easy collection or generation of high volumes of a wide variety of valuable data of different veracity. As rich sources of big data, social networks consist of users (or social entities) who are often linked by some interdependency such as `following' relationships. Since these big social networks keep growing at a high velocity, there are situations in which an individual user (or business) wants to find those frequently followed groups of social entities so that he can also follow the same groups. Discovery of these frequently followed groups can be challenging because the social networks are usually big (with lots of users/social entities) but can be sparsely distributed (with most users only know some but not all users/social entities in some portions of a social network). In this paper, we present a social network mining algorithm that uses different compressed models to space-efficiently represent social entities so as to facilitate the discovery of groups of frequently followed social entities from these big but sparsely distributed social networks. Evaluation results show the practicality of our algorithm in efficient mining of `following' patterns from big but sparsely distributed social networks.

As we are living in the era of big data, high volumes of wide varieties of data which may be of different veracity (e.g., precise data, imprecise and uncertain data) are easily generated or collected at a high velocity in many real-life applications. Embedded in these big data is valuable knowledge and useful information, which can be discovered by big data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring merchandise items and/or events. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. Hence, in this paper, we present an item-centric algorithm for mining frequent patterns from big uncertain data. In recent years, big data have been gaining the attention from the research community as driven by relevant technological innovations (e.g., clouds) and novel paradigms (e.g., social networks). As big data are typically published online to support knowledge management and fruition processes, these big data are usually handled by multiple owners with possible secure multi-part computation issues. Thus, privacy and security of big data has become a fundamental problem in this research context. In this paper, we present, not only an item-centric algorithm for mining frequent patterns from big uncertain data, but also a privacy-preserving algorithm. In other words, we present- in this paper-a privacy-preserving item-centric algorithm for mining frequent patterns from big uncertain data. Results of our analytical and empirical evaluation show the effectiveness of our algorithm in mining frequent patterns from big uncertain data in a privacy-preserving manner.

For many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining---and specially, association rule mining or web mining---is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships---in the form of association rules---among frequently occurring patterns. These algorithms include level-wise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, and vertical mining algorithms. While these algorithms are popular, they suffer from some drawbacks. Moreover, as we are living in the era of big data, high volumes of a wide variety of valuable data of different veracity collected at a high velocity post another challenges to data science and big data analytics. To deal with these big data while avoiding the drawbacks of existing algorithms, we present a bitwise parallel association rule mining system for web mining and recommendation in this paper. Evaluation results show the effectiveness and practicality of our parallel algorithm---which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them---in real-life web applications.

In the current era of big data, high volumes of valuable data can be easily collected and generated. Social networks are examples of generating sources of these big data. Users in these social networks are often linked by some interdependency such as friendship. As these big social networks keep growing, there are situations in which an individual user wants to find popular groups of friends so that he can recommend the same groups to other users. In this paper, we present a big data analytic solution that uses the MapReduce model in mining these big social networks for discovering groups of frequently connected users for friend recommendation. Evaluation results show the efficiency and practicality of our data analytic solution in mining big social networks, discovering popular users, and recommending friends.

In many applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining in general-or frequent pattern mining in specific-can be applicable. Since its introduction, frequent pattern mining has drawn attention from many researchers. Consequently, many frequent pattern mining algorithms have been proposed, which include levelwise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, as well as vertical mining algorithms. While these algorithms are popular, they also suffer from some drawbacks. To avoid these drawbacks, we propose an alternative frequent pattern mining algorithm called BW-mine in this paper. Evaluation results show that our proposed algorithm is both space-and time-efficient. Furthermore, to show the practicality of BW-mine in real-life applications, we apply BW-mine to discover popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them.