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.     

An efficient algorithm for incrementally mining frequent closed itemsets

The purpose of mining frequent itemsets is to identify the items in groups that always appear together and exceed the user-specified threshold of a transaction database. However, numerous frequent itemsets may exist in a transaction database, hindering decision making. Recently, the mining of frequent closed itemsets has become a major research issue because sets of frequent closed itemsets are condensed yet complete representations of frequent itemsets. Therefore, all frequent itemsets can be derived from a group of frequent closed itemsets. Nonetheless, the number of transactions in a transaction database can increase rapidly in a short time period, and a number of the transactions may be outdated. Thus, frequent closed itemsets may be changed with the addition of new transactions or the deletion of old transactions from the transaction database. Updating previously closed itemsets when transactions are added or removed from the transaction database is challenging. This study proposes an efficient algorithm for incrementally mining frequent closed itemsets without scanning the original database. The proposed algorithm updates closed itemsets by performing several operations on the previously closed itemsets and added/deleted transactions without searching the previously closed itemsets. The experimental results show that the proposed algorithm significantly outperforms previous methods, which require a substantial length of time to search previously closed itemsets.     

BitTableFI: An efficient mining frequent itemsets algorithm

Mining frequent itemsets in transaction databases, time-series databases and many other kinds of databases is an important task and has been studied popularly in data mining research. The problem of mining frequent itemsets can be solved by constructing a candidate set of itemsets first, and then, identifying those itemsets that meet the frequent itemset requirement within this candidate set. Most of the previous research mainly focuses on pruning to reduce the candidate itemsets amounts and the times of scanning databases. However, many algorithms adopt an Apriori-like candidate itemsets generation and support count approach that is the most time-wasted process. To address this issue, the paper proposes an effective algorithm named as BitTableFI. In the algorithm, a special data structure BitTable is used horizontally and vertically to compress database for quick candidate itemsets generation and support count, respectively. The algorithm can also be used in many Apriori-like algorithms to improve the performance. Experiments with both synthetic and real databases show that BitTableFI outperforms Apriori and CBAR which uses ClusterTable for quick support count.     

一种高效的频繁模式挖掘算法

在分析研究具有代表性的关联知识挖掘算法的基础上,提出了挖掘频繁模式的一个新的数据库存储结构AFP-树,并在此结构上设计了一个频繁模式挖掘算法。理论研究已经阐明了AFP-树的有效性和相关算法的高效性。     

An efficient algorithm for mining periodic high-utility sequential patterns

A periodic high-utility sequential pattern (PHUSP) is a pattern that not only yields a high-utility (e.g. high profit) but also appears regularly in a sequence database. Finding PHUSPs is useful for several applications such as market basket analysis, where it can reveal recurring and profitable customer behavior. Although discovering PHUSPs is desirable, it is computationally difficult. To discover PHUSPs efficiently, this paper proposes a structure for periodic high-utility sequential pattern mining (PHUSPM) named PUSP. Furthermore, to reduce the search space and speed up PHUSPM, a pruning strategy is developed. This results in an efficient algorithm called periodic high-utility sequential pattern optimal miner (PUSOM). An experimental evaluation was performed on both synthetic and real-life datasets to compare the performance of PUSOM with state-of-the-art PHUSPM algorithms in terms of execution time, memory usage and scalability. Experimental results show that the PUSOM algorithm can efficiently discover the complete set of PHUSPs. Moreover, it outperforms the other four algorithms as the former can prune many unpromising patterns using its designed structure and pruning strategy.     

An efficient algorithm for mining frequent weighted itemsets using interval word segments

Mining frequent weighted itemsets (FWIs) from weighted-item transaction databases has recently received research interest. In real-world applications, sparse weighted-item transaction databases (SWITDs) are common. For example, supermarkets have many items, but each transaction has a small number of items. In this paper, we propose an interval word segment (IWS) structure to store and process tidsets for enhancing the effectiveness of mining FWIs from SWITDs. The IWS structure allows the intersection of tidsets between two itemsets to be performed very fast. A map array is proposed for storing a 1-bit index for words. From the map array, 1-bits are mapped to create the tidset of an itemset for faster calculation of the weighted support of itemsets. Experimental results for a number of SWITDs show that the method based on IWS structure outperforms existing methods.     

一种新的多层频繁模式挖掘算法

在多层频繁模式挖掘时,结合映射和并发技术,改进经典的FP-growth算法,提出了多层映射频繁模式增长算法(ML-MFP_Growth).首先对事务数据库中的项目编码预处理,随后对编码数据库的每一列进行映射,构造各层映射频繁模式树(MFP-Tree),最后并发挖掘各层MFP-Tree,得到所有频繁模式.实验表明,ML_MFP_Growth算法比传统多层频繁模式挖掘算法性能有所提高.     

An efficient algorithm for discovering frequent subgraphs

Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in various application areas. However, as data mining techniques are being increasingly applied to nontraditional domains, existing frequent pattern discovery approaches cannot be used. This is because the transaction framework that is assumed by these algorithms cannot be used to effectively model the data sets in these domains. An alternate way of modeling the objects in these data sets is to represent them using graphs. Within that model, one way of formulating the frequent pattern discovery problem is that of discovering subgraphs that occur frequently over the entire set of graphs. We present a computationally efficient algorithm, called FSG, for finding all frequent subgraphs in large graph data sets. We experimentally evaluate the performance of FSG using a variety of real and synthetic data sets. Our results show that despite the underlying complexity associated with frequent subgraph discovery, FSG is effective in finding all frequently occurring subgraphs in data sets containing more than 200,000 graph transactions and scales linearly with respect to the size of the data set.     

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.     

TFP: an efficient algorithm for mining top-k frequent closed itemsets

Frequent itemset mining has been studied extensively in literature. Most previous studies require the specification of a min/spl I.bar/support threshold and aim at mining a complete set of frequent itemsets satisfying min/spl I.bar/support. However, in practice, it is difficult for users to provide an appropriate min/spl I.bar/support threshold. In addition, a complete set of frequent itemsets is much less compact than a set of frequent closed itemsets. In this paper, we propose an alternative mining task: mining top-k frequent closed itemsets of length no less than min/spl I.bar/l, where k is the desired number of frequent closed itemsets to be mined, and min/spl I.bar/l is the minimal length of each itemset. An efficient algorithm, called TFP, is developed for mining such itemsets without mins/spl I.bar/support. Starting at min/spl I.bar/support = 0 and by making use of the length constraint and the properties of top-k frequent closed itemsets, min/spl I.bar/support can be raised effectively and FP-Tree can be pruned dynamically both during and after the construction of the tree using our two proposed methods: the closed node count and descendant/spl I.bar/sum. Moreover, mining is further speeded up by employing a top-down and bottom-up combined FP-Tree traversing strategy, a set of search space pruning methods, a fast 2-level hash-indexed result tree, and a novel closed itemset verification scheme. Our extensive performance study shows that TFP has high performance and linear scalability in terms of the database size.     

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.     

一种高效的最大频繁项挖掘算法

提出一种下三角矩阵的最大频繁项挖掘算法DTMFIM,首先将事务数据库映射到一个布尔矩阵中,并产生频繁1-项集,然后根据频繁1-项集生产频繁2-项集,且对其结果用下三角的布尔矩阵存储,最大频繁项集可通过这个下三解矩阵求得,在求解过程中不断地压缩这个下三角矩阵.实验表明,此算法实现简洁、高效,与经典的Apriori算法及部分Apfiori改进算法相比,特别对大事务集、长项目集数据挖掘效果更为明显.     

DSM-FI: an efficient algorithm for mining frequent itemsets in data streams

Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions. According to the proposed algorithm, each transaction of the stream is projected into a set of sub-transactions, and these sub-transactions are inserted into a new in-memory summary data structure, called SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far. Finally, the set of all frequent itemsets is determined from the current SFI-forest. Theoretical analysis and experimental studies show that the proposed DSM-FI algorithm uses stable memory, makes only one pass over an online transactional data stream, and outperforms the existing algorithms of one-pass mining of frequent itemsets.

An efficient algorithm for matching multiple patterns

An efficient algorithm for performing multiple pattern match in a string is described. The match algorithm combines the concept of deterministic finite state automata (DFSA) and the Boyer-Moore algorithm to achieve better performance. Experimental results indicate that in the average case, the algorithm is able to perform pattern match operations sublinearly, i.e. it does not need to inspect every character of the string to perform pattern match operations. The analysis shows that the number of characters to be inspected decreases as the length of patterns increases, and increases slightly as the total number of patterns increases. To match an eight-character pattern in an English string using the algorithm, only about 17% of all characters of the strong and 33% of all characters of the string, when the number of patterns is seven, are inspected. In an actual testing, the algorithm running on SUN 3/160 takes only 3.7 s to search seven eight-character patterns in a 1.4-Mbyte English text file     

快速挖掘频繁项目集算法

频繁项目集挖掘是数据挖掘领域最重要的基本问题之一,在分析已有算法的基础上,提出了一种能够快速挖掘频繁项目集的算法,对频繁项目集挖掘的搜索空间以及数据表示进行了优化,缩小搜索空间和数据表示的规模,减少计算项目集支持数的时间,提高算法的执行效率,实验结果表明,该算法在性能上优于FP-Growth算法.     

MFPSM:基于双向约束的极大频繁页面集挖掘算法

挖掘极大频繁页面集是WEB使用挖掘中的关键应用之一。由于一定时间段的会话中蕴含着用户的访问模式与访问动机,设计一种结点带有驻留时间,类似FP-tree的频繁页面树FPDT-tree结构;利用FPDT-tree结构存储双向驻留时间约束的会话数据库,简化挖掘过程中驻留时间阈值的设置。基于FPDT-tree提出算法MFPSM挖掘会话中的极大频繁页面集,实验结果表明,在时间约束环境中,通过决策者给出合适的时间约束阈值,该算法可以有效地缩短挖掘极大频繁页面集的时间。     

A data mining proxy approach for efficient frequent itemset mining

Data mining has attracted a lot of research efforts during the past decade. However, little work has been reported on the efficiency of supporting a large number of users who issue different data mining queries periodically when there are new needs and when data is updated. Our work is motivated by the fact that the pattern-growth method is one of the most efficient methods for frequent pattern mining which constructs an initial tree and mines frequent patterns on top of the tree. In this paper, we present a data mining proxy approach that can reduce the I/O costs to construct an initial tree by utilizing the trees that have already been resident in memory. The tree we construct is the smallest for a given data mining query. In addition, our proxy approach can also reduce CPU cost in mining patterns, because the cost of mining relies on the sizes of trees. The focus of the work is to construct an initial tree efficiently. We propose three tree operations to construct a tree. With a unique coding scheme, we can efficiently project subtrees from on-disk trees or in-memory trees. Our performance study indicated that the data mining proxy significantly reduces the I/O cost to construct trees and CPU cost to mine patterns over the trees constructed.     

Computing the minimum-support for mining frequent patterns

Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users’ mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements. This work is partially supported by Australian ARC grants for discovery projects (DP0449535, DP0559536 and DP0667060), a China NSF Major Research Program (60496327), a China NSF grant (60463003), an Overseas Outstanding Talent Research Program of the Chinese Academy of Sciences (06S3011S01), and an Overseas-Returning High-level Talent Research Program of China Human-Resource Ministry. A preliminary and shortened version of this paper has been published in the Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence (PRICAI ’04).     

A non-group parallel frequent pattern mining algorithm based on conditional patterns

Frontiers of Information Technology & Electronic Engineering - Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and...