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

DiffNodesets: An Efficient Structure for Fast Mining Frequent Itemsets

Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for mining frequent itemsets. In this paper, we propose DiffNodeset, a novel and more efficient itemset representation, for mining frequent itemsets. Based on the DiffNodeset structure, we present an efficient algorithm, named dFIN, to mining frequent itemsets. To achieve high efficiency, dFIN finds frequent itemsets using a set-enumeration tree with a hybrid search strategy and directly enumerates frequent itemsets without candidate generation under some case. For evaluating the performance of dFIN, we have conduct extensive experiments to compare it against with existing leading algorithms on a variety of real and synthetic datasets. The experimental results show that dFIN is significantly faster than these leading algorithms.Comment: 22 pages, 13 figure

CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams

Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad,an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad's memory imprint is much lower and its performances globally better than competitor methods.Comment: KDD2

Mining High Utility Patterns Over Data Streams

Mining useful patterns from sequential data is a challenging topic in data mining. An important task for mining sequential data is sequential pattern mining, which discovers sequences of itemsets that frequently appear in a sequence database. In sequential pattern mining, the selection of sequences is generally based on the frequency/support framework. However, most of the patterns returned by sequential pattern mining may not be informative enough to business people and are not particularly related to a business objective. In view of this, high utility sequential pattern (HUSP) mining has emerged as a novel research topic in data mining recently. The main objective of HUSP mining is to extract valuable and useful sequential patterns from data by considering the utility of a pattern that captures a business objective (e.g., profit, users interest). In HUSP mining, the goal is to find sequences whose utility in the database is no less than a user-specified minimum utility threshold. Nowadays, many applications generate a huge volume of data in the form of data streams. A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. Mining HUSP from such data poses many challenges. First, it is infeasible to keep all streaming data in the memory due to the high volume of data accumulated over time. Second, mining algorithms need to process the arriving data in real time with one scan of data. Third, depending on the minimum utility threshold value, the number of patterns returned by a HUSP mining algorithm can be large and overwhelms the user. In general, it is hard for the user to determine the value for the threshold. Thus, algorithms that can find the most valuable patterns (i.e., top-k high utility patterns) are more desirable. Mining the most valuable patterns is interesting in both static data and data streams. To address these research limitations and challenges, this dissertation proposes techniques and algorithms for mining high utility sequential patterns over data streams. We work on mining HUSPs over both a long portion of a data stream and a short period of time. We also work on how to efficiently identify the most significant high utility patterns (namely, the top-k high utility patterns) over data streams. In the first part, we explore a fundamental problem that is how the limited memory space can be well utilized to produce high quality HUSPs over the entire data stream. An approximation algorithm, called MAHUSP, is designed which employs memory adaptive mechanisms to use a bounded portion of memory, to efficiently discover HUSPs over the entire data streams. The second part of the dissertation presents a new sliding window-based algorithm to discover recent high utility sequential patterns over data streams. A novel data structure named HUSP-Tree is proposed to maintain the essential information for mining recenT HUSPs. An efficient and single-pass algorithm named HUSP-Stream is proposed to generate recent HUSPs from HUSP-Tree. The third part addresses the problem of top-k high utility pattern mining over data streams. Two novel methods, named T-HUDS and T-HUSP, for finding top-k high utility patterns over a data stream are proposed. T-HUDS discovers top-k high utility itemsets and T-HUSP discovers top-k high utility sequential patterns over a data stream. T-HUDS is based on a compressed tree structure, called HUDS-Tree, that can be used to efficiently find potential top-k high utility itemsets over data streams. T-HUSP incrementally maintains the content of top-k HUSPs in a data stream in a summary data structure, named TKList, and discovers top-k HUSPs efficiently. All of the algorithms are evaluated using both synthetic and real datasets. The performances, including the running time, memory consumption, precision, recall and Fmeasure, are compared. In order to show the effectiveness and efficiency of the proposed methods in reallife applications, the fourth part of this dissertation presents applications of one of the proposed methods (i.e., MAHUSP) to extract meaningful patterns from a real web clickstream dataset and a real biosequence dataset. The utility-based sequential patterns are compared with the patterns in the frequency/support framework. The results show that high utility sequential pattern mining provides meaningful patterns in real-life applications