An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model

AbstractFrequent itemset mining from data streams is an important data mining problem with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. However, it is also a difficult problem due to the unbounded, high-speed and continuous characteristics of streaming data. Therefore, extracting frequent itemsets from more recent data can enhance the analysis of stream data. In this paper, we propose an efficient algorithm, called Max-FISM (Maximal-Frequent Itemsets Mining), for mining recent maximal frequent itemsets from a high-speed stream of transactions within a sliding window. According to our algorithm, whenever a new transaction is inserted in the current window only its maximum itemset should be inserted into a prefix tree-based summary data structure called Max-Set for maintaining the number of independent appearance of each transaction in the current window. Finally, the set of recent maximal frequent itemsets is obtained from the current Max-Set. Experimental studies show that the proposed Max-FISM algorithm is highly efficient in terms of memory and time complexity for mining recent maximal frequent itemsets over high-speed data streams

Techniques for improving clustering and association rules mining from very large transactional databases

Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have been developed, no algorithm is better than others on all aspects, such as accuracy, efficiency, scalability, adaptability and memory usage. While more efficient and effective algorithms need to be developed for handling the large-scale and complex stored datasets, emerging applications where data takes the form of streams pose new challenges for the data mining community. The existing techniques and algorithms for static stored databases cannot be applied to the data streams directly. They need to be extended or modified, or new methods need to be developed to process the data streams.In this thesis, algorithms have been developed for improving efficiency and accuracy of clustering and association rules mining on very large, high dimensional, high cardinality, sparse transactional databases and data streams.A new similarity measure suitable for clustering transactional data is defined and an incremental clustering algorithm, INCLUS, is proposed using this similarity measure. The algorithm only scans the database once and produces clusters based on the user’s expectations of similarities between transactions in a cluster, which is controlled by the user input parameters, a similarity threshold and a support threshold. Intensive testing has been performed to evaluate the effectiveness, efficiency, scalability and order insensitiveness of the algorithm.To extend INCLUS for transactional data streams, an equal-width time window model and an elastic time window model are proposed that allow mining of clustering changes in evolving data streams. The minimal width of the window is determined by the minimum clustering granularity for a particular application. Two algorithms, CluStream_EQ and CluStream_EL, based on the equal-width window model and the elastic window model respectively, are developed by incorporating these models into INCLUS. Each algorithm consists of an online micro-clustering component and an offline macro-clustering component. The online component writes summary statistics of a data stream to the disk, and the offline components uses those summaries and other user input to discover changes in a data stream. The effectiveness and scalability of the algorithms are evaluated by experiments.This thesis also looks into sampling techniques that can improve efficiency of mining association rules in a very large transactional database. The sample size is derived based on the binomial distribution and central limit theorem. The sample size used is smaller than that based on Chernoff Bounds, but still provides the same approximation guarantees. The accuracy of the proposed sampling approach is theoretically analyzed and its effectiveness is experimentally evaluated on both dense and sparse datasets.Applications of stratified sampling for association rules mining is also explored in this thesis. The database is first partitioned into strata based on the length of transactions, and simple random sampling is then performed on each stratum. The total sample size is determined by a formula derived in this thesis and the sample size for each stratum is proportionate to the size of the stratum. The accuracy of transaction size based stratified sampling is experimentally compared with that of random sampling.The thesis concludes with a summary of significant contributions and some pointers for further work

Discovering Interesting Patterns and Associations in Data Streams

A data stream is a sequence of items that arrive in a timely order. Different from data in traditional static databases, data streams are continuous, unbounded, usually come with high speed, and have a data value distribution that often changes with time (Guha, 2001). As more applications such as web transactions, telephone records, and network flows generate a large number of data streams every day, efficient knowledge discovery of data streams is an active and growing research area in data mining with broad applications. Traditional data mining algorithms are developed to work on a complete static dataset and, thus, cannot be applied directly in data stream applications.One area of data mining research is to mine association relationship in a data set. Most of association mining techniques for data streams can be categorized into two types: those developed based on frequent patterns and those developed based on closed patterns. Due to the number of frequent patterns are often huge and redundant, non-informative patterns are contained in frequent patterns. An alternative way is to develop the association mining approaches for data streaming applications based on closed patterns, which generally represent a small subset of all frequent patterns, but provide complete and condensed information. In these researches, the closed pattern mining is the prerequisite condition for non-redundant and informative association mining.In this dissertation, a sliding window technique for dynamic mining of closed patterns in data streams is proposed, and an approach of mining non-redundant and informative associations based on the discovered closed patterns is developed. The closed pattern and relevant association mining techniques are selected research area in this dissertation. First, the closed patterns for a given collection of data are currently the most compact data knowledge that can provide complete support information for all data patterns.Compared with other techniques, the proposed closed pattern mining technique has potential to largely decrease the number of subsequent combinatorial calculations performed on the data patterns. Second, the memory requirement to store the closed patterns and relevant associations is generally lower than the corresponding frequent patterns and associations. In some data streaming applications, memory usage is an important measurement, because in these applications memory usage is the bottleneck for knowledge discovery. Third, the associations generated for data streams are the knowledge used to identify the relations within the data. The discovered relations can find their wide applications in many data streaming environments.Different from the closed pattern mining techniques on traditional databases, which require multiple scans of the entire database, the proposed technique determines the closed patterns with a single scan. It is an incremental mining process; as the sliding window advances, new data transactions enter and old data transactions exit the window. But instead of regenerating closed patterns from the entire window, the proposed technique updates the old set of closed patterns whenever a new transaction arrives and/or an old transaction leaves the sliding window to obtain the current set of closed patterns. This incremental feature allows the user to get the most recent updated closed patterns without rescanning the entire updated database, which saves not only the computation time, but more importantly, the I/O operating time to load and write data from database to memory. Third, the proposed sliding window technique can handle both the insertion and deletion operations independently, which allows the user to adjust the sliding window size in different application environments. Furthermore, the proposed interesting patterns and association mining framework can handle different users' requests at the same time at their specified support and confidence thresholds, and interested input and output patterns.The research includes both theoretical proofs of correctness for the proposed algorithms and simulation experiments to compare the proposed techniques with those existing in the literature using synthetic and real datasets. The utility of the proposed technique is applied to sensor network databases of a traffic management and an environmental monitoring site for missing data estimation purpose