As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

DiSE-growth, a tree-based (pattern-growth) algorithm for mining DIverse Social Entities, is proposed and experimentally assessed in this paper. The algorithm makes use of a specialized data structure, called DiSE-tree, for effectively and efficiently representing relevant information on diverse social entities while successfully supporting the mining phase. Diverse entities are popular in a wide spectrum of application scenarios, ranging from linked Web data to Semantic Web and social networks. In all these application scenarios, it has become important to analyze high volumes of valuable linked data and discover those diverse social entities. We complement our analytical contributions by means of an experimental evaluation that clearly shows the benefits of our tree-based diverse social entity mining algorithm.

Due to advances in technology, high volumes of valuable data can be collected and transmitted at high velocity in various scientific and engineering applications. Consequently, efficient data mining algorithms are in demand for analyzing these data. For instance, frequent pattern mining discovers implicit, previously unknown, and potentially useful knowledge about relationships among frequently co-occurring items, objects and/or events. While many frequent pattern mining algorithms handle precise data, there are situations in which data are uncertain. In recent years, tree-based algorithms for mining uncertain data have been developed. However, tree structures corresponding to these algorithms can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of loose upper bounds on expected supports. In this paper, we propose (i) a compact tree structure for capturing uncertain data, (ii) a technique for using our tree structure to tighten upper bounds to expected support, and (iii) an algorithm for mining frequent patterns based on our tightened bounds. Experimental results show the benefits of our tightened upper bounds to expected supports in uncertain frequent pattern mining.

As frequent pattern mining plays an important role in various real-life applications, it has been the subject of numerous studies. Most of the studies mine transactional datasets of precise data. However, there are situations in which data are uncertain. Over the few years, Apriori-based, tree-based, and hyperlinked array structure based mining algorithms have been proposed to mine frequent patterns from these probabilistic datasets of uncertain data. These algorithms view the datasets "horizontally" as collections of transactions, and each records a set of items contained in that transaction. In this paper, we consider an alternative representation such that probabilistic datasets of uncertain data can be viewed "vertically" as collections of vectors. The vector for each item indicates which transactions contain that item. We also propose an algorithm called U-VIPER to mine these probabilistic datasets "vertically for frequent patterns.

Over the past few years, the rapid growth and the exponential use of social digital media has led to an increase in popularity of social networks and the emergence of social computing. In general, social networks are structures made of social entities (e.g., individuals) that are linked by some specific types of interdependency such as friendship. Most users of social media (e.g., Face book, Google+, Linked In, My Space, Twitter) have many linkages in terms of friends, connections, and/or followers. Among all these linkages, some of them are more important than another. For instance, some friends of a user may be casual ones who acquaintances met him at some points in time, whereas some others may be friends that care about him in such a way that they frequently post on his wall, view his updated profile, send him messages, invite him for events, and/or follow his tweets. In this paper, we apply data mining techniques to social networks to help users of the social digital media to distinguish these important friends from a large number of friends in their social networks.

The share measure of item sets has been proposed to discover useful knowledge about numerical values associated with items in a transaction database. Therefore, share-frequent pattern mining problem becomes a very important research issue in data mining. However, the existing algorithms of share-frequent pattern mining are based on static databases. Moreover, they are not suitable for interactive mining. In this paper, we propose a novel tree structure IncrShrFP-Tree (Incremental Share-Frequent Pattern Tree) for incremental and interactive share-frequent pattern mining. It is effective for incremental and interactive mining to utilize the previous tree structure and to use the previous mining results when a database is updated or a minimum support threshold is changed. It needs maximum two database scans to calculate the resultant share-frequent patterns in incremental databases. Extensive performance analyses show that our method is very efficient for incremental and interactive share-frequent pattern mining.

Recently proposed regular pattern mining provides an effective technique to find patterns occurring at regular interval in a static database. However, the occurrence characteristic of patterns may change significantly with the update of database. Therefore, this paper proposes the Incremental Regular Pattern Tree (IncRT) and a pattern growth mining technique to find regular patterns on incremental transactional databases. Experiment results show the effectiveness of the proposed method.

Mining web access sequences can discover very useful knowledge from web logs with broad applications. By considering non-binary occurrences of web pages as internal utilities in web access sequences, e.g., time spent by each user in a web page, more realistic information can be extracted. However, the existing utility-based approach has many limitations such as considering only forward references of web access sequences, not applicable for incremental mining, suffers in the level-wise candidate generation-and-test methodology, needs several database scans and does not show how to mine web traversal sequences with external utility, i.e., different impacts/significances for different web pages. In this paper, we propose a new approach to solve these problems. Moreover, we propose two novel tree structures, called UWAS-tree (utility-based web access sequence tree), and IUWAS-tree (incremental UWAS tree), for mining web access sequences in static and dynamic databases respectively. Our approach can handle both forward and backward references, static and dynamic data, avoids the level-wise candidate generation-and-test methodology, does not scan databases several times and considers both internal and external utilities of a web page. Extensive performance analyses show that our approach is very efficient for both static and incremental mining of high utility web access sequences.

By considering different weights of the items, weighted frequent pattern (WFP)mining can discover more important knowledge compared to traditional frequent pattern mining. Therefore, WFP mining becomes an important research issue in data mining and knowledge discovery area. However, the existing algorithms cannot be applied for stream data mining because they require multiple database scans. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively. In this paper, we propose a sliding window based novel technique WFPMDS (weighted frequent pattern mining over data streams) using a single scan of data stream to discover important knowledge form the recent data elements. Extensive performance analyses show that our technique is very efficient for WFP mining over data streams.

Recently, a significant number of parallel and distributed algorithms have been proposed to mine frequent patterns (FP) from large and/or distributed databases. Among them parallelization of the FP-growth algorithms using the FP-tree has been proved to be highly efficient. However, the FP-tree-based techniques suffer from two major limitations such as multiple database scans requirement (i.e., high I/O cost) and high inter-processor communications cost (during the mining phase). Therefore, we propose a novel tree structure, called PP-tree (Parallel Pattern tree) that significantly reduces the I/O cost by capturing the database contents with a single scan and facilitates the efficient FP-growth mining on it with reduced inter-processor communication overhead. Our parallel algorithm works independently at each local site and locally generates global frequent patterns which are merged at the final stage. The experimental results reflect that parallel and distributed FP mining with PP-tree outperforms other state-of-the-art algorithms.

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree ({\rm IHUP}_{{\rm {L}}}-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree ({\rm IHUP}_{{\rm {TF}}}-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree ({\rm IHUP}_{{\rm {TWU}}}-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.

This paper proposes a prefix-tree structure, called CPS-tree (Compact Pattern Stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. The CPS-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient FP-growth-based [1] mining technique.