Top-down mining of frequent closed patterns from very high dimensional data

Frequent pattern mining is an essential theme in data mining. Existing algorithms usually use a bottom-up search strategy. However, for very high dimensional data, this strategy cannot fully utilize the minimum support constraint to prune the rowset search space. In this paper, we propose a new method called top-down mining together with a novel row enumeration tree to make full use of the pruning power of the minimum support constraint. Furthermore, to efficiently check if a rowset is closed, we develop a method called the trace-based method. Based on these methods, an algorithm called TD-Close is designed for mining a complete set of frequent closed patterns. To enhance its performance further, we improve it by using new pruning strategies and new data structures that lead to a new algorithm TTD-Close. Our performance study shows that the top-down strategy is effective in cutting down search space and saving memory space, while the trace-based method facilitates the closeness-checking. As a result, the algorithm TTD-Close outperforms the bottom-up search algorithms such as Carpenter and FPclose in most cases. It also runs faster than TD-Close.     

An efficient algorithm for mining frequent inter-transaction patterns

In this paper, we propose an efficient method for mining all frequent inter-transaction patterns. The method consists of two phases. First, we devise two data structures: a dat-list, which stores the item information used to find frequent inter-transaction patterns; and an ITP-tree, which stores the discovered frequent inter-transaction patterns. In the second phase, we apply an algorithm, called ITP-Miner (Inter-Transaction Patterns Miner), to mine all frequent inter-transaction patterns. By using the ITP-tree, the algorithm requires only one database scan and can localize joining, pruning, and support counting to a small number of dat-lists. The experiment results show that the ITP-Miner algorithm outperforms the FITI (First Intra Then Inter) algorithm by one order of magnitude.     

DBC: a condensed representation of frequent patterns for efficient mining

Given a large set of data, a common data mining problem is to extract the frequent patterns occurring in this set. The idea presented in this paper is to extract a condensed representation of the frequent patterns called disjunction-bordered condensation (DBC), instead of extracting the whole frequent pattern collection. We show that this condensed representation can be used to regenerate all frequent patterns and their exact frequencies. Moreover, this regeneration can be performed without any access to the original data. Practical experiments show that the DBCcan be extracted very efficiently even in difficult cases and that this extraction and the regeneration of the frequent patterns is much more efficient than the direct extraction of the frequent patterns themselves. We compared the DBC with another representation of frequent patterns previously investigated in the literature called frequent closed sets. In nearly all experiments we have run, the DBC have been extracted much more efficiently than frequent closed sets. In the other cases, the extraction times are very close.     

挖掘事务间频繁闭项集的高效率算法

事务间频繁项集将传统的单维事务内关联规则扩展到多维跨事务关联规则,但事务问频繁项集的数量随滑 动时同间窗口的增大而迅速增加.利用频繁闭项集的特点.提出事务间频繁闭项集的概念及其挖掘算法(FCITA).该算法采用分割和条件数据库技术,避免生成庞大的扩展数据库;利用扩展二进制形武压缩事务,从而提高支持度的计算效事.此外,动态排序和哈希表极大地减少了频繁闭项集的测试次数.仿真比较表明,FCITA算法具有较高的挖掘效率.     

一种基于变尺度滑动窗口的数据流频繁集挖掘算法

基干传统滑动窗口机制的数据流频繁集挖掘算法较多地考虑快速且精确的效果,而较少考虑数据流的时变特性,对传统的滑动窗口机制进行改进.同时考虑数据流的海量特性和时变特性,提出一种基于变尺度滑动窗口机制的数据流频繁集挖掘算法V-Stream.该算法采用事务链表组的概要数据结构.能够根据数据流的数据分布变化自适应调整窗口大小.Eclipse上的仿真实验结果表明,V-Stream相比Manku算法提高了挖掘数据流频繁集的时间与空间效率.     

Efficient single-pass frequent pattern mining using a prefix-tree

The FP-growth algorithm using the FP-tree has been widely studied for frequent pattern mining because it can dramatically improve performance compared to the candidate generation-and-test paradigm of Apriori. However, it still requires two database scans, which are not consistent with efficient data stream processing. In this paper, we present a novel tree structure, called CP-tree (compact pattern tree), that captures database information with one scan (insertion phase) and provides the same mining performance as the FP-growth method (restructuring phase). The CP-tree introduces the concept of dynamic tree restructuring to produce a highly compact frequency-descending tree structure at runtime. An efficient tree restructuring method, called the branch sorting method, that restructures a prefix-tree branch-by-branch, is also proposed in this paper. Moreover, the CP-tree provides full functionality for interactive and incremental mining. Extensive experimental results show that the CP-tree is efficient for frequent pattern mining, interactive, and incremental mining with a single database scan.     

Privacy-preserving algorithms for distributed mining of frequent itemsets

Standard algorithms for association rule mining are based on identification of frequent itemsets. In this paper, we study how to maintain privacy in distributed mining of frequent itemsets. That is, we study how two (or more) parties can find frequent itemsets in a distributed database without revealing each party’s portion of the data to the other. The existing solution for vertically partitioned data leaks a significant amount of information, while the existing solution for horizontally partitioned data only works for three parties or more. In this paper, we design algorithms for both vertically and horizontally partitioned data, with cryptographically strong privacy. We give two algorithms for vertically partitioned data; one of them reveals only the support count and the other reveals nothing. Both of them have computational overheads linear in the number of transactions. Our algorithm for horizontally partitioned data works for two parties and above and is more efficient than the existing solution.     

Mining frequent patterns with the pattern tree

Mining frequent patterns with a frequent pattern tree (FP-tree in short) avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves much better performance and efficiency than Apriori-like algorithms. However, the database still needs to be scanned twice to get the FP-tree. This can be very time-consuming when new data is added to an existing database because two scans may be needed for not only the new data but also the existing data. In this research we propose a new data structure, the pattern tree (P-tree in short), and a new technique, which can get the P-tree through only one scan of the database and can obtain the corresponding FP-tree with a specified support threshold. Updating a P-tree with new data needs one scan of the new data only, and the existing data does not need to be re-scanned. Our experiments show that the P-tree method outperforms the FP-tree method by a factor up to an order of magnitude in large datasets. A preliminary version of this paper has been published in theProceedings of the 2002 IEEE International Conference on Data Mining (ICDM ’02), 629–632. Hao Huang: He is pursuing his Ph.D. degree in the Department of Computer Science at the University of Virginia. His research interests are Gird Computing, Data Mining and their applications in Bioinformatics. He received his M.S. in Computer Science from Colorado School of Mines in 2001. Xindong Wu, Ph.D.: He is Professor and Chair of the Department of Computer Science at the University of Vermont, USA. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW. Dr. Wu is the Executive Editor (January 1, 1999-December 31, 2004) and an Honorary Editor-in-Chief (starting January 1, 2005) of Knowledge and Information Systems (a peer-reviewed archival journal published by Springer), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP), and the Chair of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI). He served as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering (TKDE) between January 1, 2000 and December 31, 2003, and is the Editor-in-Chief of TKDE since January 1, 2005. He is the winner of the 2004 ACM SIGKDD Service Award. Richard Relue, Ph.D.: He received his Ph.D. in Computer Science from the Colorado School of Mines in 2003. His research interests include association rules in data mining, neural networks for automated classification, and artificial intelligence for robot navigation. He has been an Information Technology consultant since 1992, working with Ball Aerospace and Technology, Rational Software, Natural Fuels Corporation, and Western Interstate Commission for Higher Education (WICHE).     

Frequent pattern mining: current status and future directions

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications. The work was supported in part by the U.S. National Science Foundation NSF IIS-05-13678/06-42771 and NSF BDI-05-15813. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies.