Discovering fuzzy time-interval sequential patterns in sequence databases.

Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items. An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters.     

Mining the change of customer behavior in fuzzy time-interval sequential patterns

Comprehending changes of customer behavior is an essential problem that must be faced for survival in a fast-changing business environment. Particularly in the management of electronic commerce (EC), many companies have developed on-line shopping stores to serve customers and immediately collect buying logs in databases. This trend has led to the development of data-mining applications. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. To take a shopping example, (Bread, Short, Milk, Long, Jam), means that Bread is bought before Milk in a Short period, and Jam is bought after Milk in a Long period, where Short and Long are predetermined linguistic terms given by managers. This information shown in this example reveals more general and concise knowledge for managers, allowing them to make quick-response decisions, especially in business. However, no studies, to our knowledge, have yet to address the issue of changes in fuzzy time-interval sequential patterns. The fuzzy time-interval sequential pattern, (Bread, Short, Milk, Long, Jam), became available in last year; however, is not a trend this year, and has been substituted by (Bread, Short, Yogurt, Short, Jam). Without updating this knowledge, managers might map out inappropriate marketing plans for products or services and dated inventory strategies with respect to time-intervals. To deal with this problem, we propose a novel change mining model, MineFuzzChange, to detect the change in fuzzy time-interval sequential patterns. Using a brick-and-mortar transactional dataset collected from a retail chain in Taiwan and a B2C EC dataset, experiments are carried out to evaluate the proposed model. We empirically demonstrate how the model helps managers to understand the changing behaviors of their customers and to formulate timely marketing and inventory strategies.     

Mining sequential patterns from multidimensional sequence data

The problem addressed in This work is to discover the frequently occurred sequential patterns from databases. Although much work has been devoted to this subject, to the best of our knowledge, no previous research was able to find sequential patterns from d-dimensional sequence data, where d>2. Without such a capability, many practical data would be impossible to mine. For example, an online stock-trading site may have a customer database, where each customer may visit a Web site in a series of days; each day takes a series of sessions and each session visits a series of Web pages. Then, the data for each customer forms a 3-dimensional list, where the first dimension is days, the second is sessions, and the third is visited pages. To mine sequential patterns from this kind of sequence data, two efficient algorithms have been developed in This work.     

Mining sequential patterns across multiple sequence databases

In this paper, given a set of sequence databases across multiple domains, we aim at mining multi-domain sequential patterns, where a multi-domain sequential pattern is a sequence of events whose occurrence time is within a pre-defined time window. We first propose algorithm Naive in which multiple sequence databases are joined as one sequence database for utilizing traditional sequential pattern mining algorithms (e.g., PrefixSpan). Due to the nature of join operations, algorithm Naive is costly and is developed for comparison purposes. Thus, we propose two algorithms without any join operations for mining multi-domain sequential patterns. Explicitly, algorithm IndividualMine derives sequential patterns in each domain and then iteratively combines sequential patterns among sequence databases of multiple domains to derive candidate multi-domain sequential patterns. However, not all sequential patterns mined in the sequence database of each domain are able to form multi-domain sequential patterns. To avoid the mining cost incurred in algorithm IndividualMine, algorithm PropagatedMine is developed. Algorithm PropagatedMine first performs one sequential pattern mining from one sequence database. In light of sequential patterns mined, algorithm PropagatedMine propagates sequential patterns mined to other sequence databases. Furthermore, sequential patterns mined are represented as a lattice structure for further reducing the number of sequential patterns to be propagated. In addition, we develop some mechanisms to allow some empty sets in multi-domain sequential patterns. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted. Experimental results show that by exploring propagation and lattice structures, algorithm PropagatedMine outperforms algorithm IndividualMine in terms of efficiency (i.e., the execution time).     

使用有向图挖掘时间间隔序列模式

在Chen等人提出的时间间隔序列模式概念的基础上,给出了一种利用有向图搜索时间间隔序列模式的算法。实验表明所提出的算法较I-Apriori算法更加快速和高效。     

An efficient approach for finding weighted sequential patterns from sequence databases

Weighted sequential pattern mining has recently been discussed in the field of data mining. Different from traditional sequential pattern mining, this kind of mining considers different significances of items in real applications, such as cost or profit. Most of the related studies adopt the maximum weighted upper-bound model to find weighted sequential patterns, but they generate a large number of unpromising candidate subsequences. In this study, we thus propose an efficient approach for finding weighted sequential patterns from sequence databases. In particular, a tightening strategy in the proposed approach is proposed to obtain more accurate weighted upper-bounds for subsequences in mining. Through the experimental evaluation, the results also show the proposed approach has good performance in terms of pruning effectiveness and execution efficiency.     

带数据项约束的序列模式挖掘

传统的数据挖掘方法会生成大量的模式和规则,且难以理解,而实际上用户感兴趣的只是其中的一小部分.针对该问题,在挖掘序列模式的PrefixSpan算法基础上提出一种带数据项约束的序列模式挖掘方法,通过数据项约束,减少了搜索空间.实验结果表明,该方法可以有效地挖掘出满足数据项约束的序列模式.     

Mining sequential patterns with itemset constraints

Mining sequential patterns is used to discover all the frequent sequences in a sequence database. However, the mining may return a huge number of patterns, while the users are only interested in a particular subset of these. In this paper, we consider the problem of mining sequential patterns with itemset constraints. In order to solve this problem, we propose a new algorithm named MSPIC-DBV, which is a pattern-growth algorithm that uses prefixes and dynamic bit vectors. This algorithm prunes the search space at the beginning and during the mining process. Moreover, it reduces the number of candidates that need to be checked. The experimental results show that the proposed algorithm outperforms the previous methods.     

Mining interval sequential patterns

The main task of mining sequential patterns is to analyze the transaction database of a company in order to find out the priorities of items that most customers take when consuming. In this article, we propose a new method—the ISP Algorithm. With this method, we can find out not only the order of consumer items of each customer, but also offer the periodic interval of consumer items of each customer. Compared with other previous periodic association rules, the difference is that the period the algorithm provides is not the repeated purchases in a regular time, but the possible repurchases within a certain time frame. The algorithm utilizes the transaction time interval of individual customers and that of all the customers to find out when and who will buy goods, and what items of goods they will buy. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 359–373, 2005.     

Mining conjunctive sequential patterns

In this paper we aim at extending the non-derivable condensed representation in frequent itemset mining to sequential pattern mining. We start by showing a negative example: in the context of frequent sequences, the notion of non-derivability is meaningless. Therefore, we extend our focus to the mining of conjunctions of sequences. Besides of being of practical importance, this class of patterns has some nice theoretical properties. Based on a new unexploited theoretical definition of equivalence classes for sequential patterns, we are able to extend the notion of a non-derivable itemset to the sequence domain. We present a new depth-first approach to mine non-derivable conjunctive sequential patterns and show its use in mining association rules for sequences. This approach is based on a well known combinatorial theorem: the Möbius inversion. A performance study using both synthetic and real datasets illustrates the efficiency of our mining algorithm. These new introduced patterns have a high-potential for real-life applications, especially for network monitoring and biomedical fields with the ability to get sequential association rules with all the classical statistical metrics such as confidence, conviction, lift etc.     

Mining sequential patterns with periodic wildcard gaps

Mining frequent patterns with periodic wildcard gaps is a critical data mining problem to deal with complex real-world problems. This problem can be described as follows: given a subject sequence, a pre-specified threshold, and a variable gap-length with wildcards between each two consecutive letters. The task is to gain all frequent patterns with periodic wildcard gaps. State-of-the-art mining algorithms which use matrices or other linear data structures to solve the problem not only consume a large amount of memory but also run slowly. In this study, we use an Incomplete Nettree structure (the last layer of a Nettree which is an extension of a tree) of a sub-pattern P to efficiently create Incomplete Nettrees of all its super-patterns with prefix pattern P and compute the numbers of their supports in a one-way scan. We propose two new algorithms, MAPB (Mining sequentiAl Pattern using incomplete Nettree with Breadth first search) and MAPD (Mining sequentiAl Pattern using incomplete Nettree with Depth first search), to solve the problem effectively with low memory requirements. Furthermore, we design a heuristic algorithm MAPBOK (MAPB for tOp-K) based on MAPB to deal with the Top-K frequent patterns for each length. Experimental results on real-world biological data demonstrate the superiority of the proposed algorithms in running time and space consumption and also show that the pattern matching approach can be employed to mine special frequent patterns effectively.     

带有间隔约束的多序列模式挖掘

研究这样一个问题：给定多序列、支持度阈值和间隔约束,从多序列中挖掘所有出现次数不小于支持度阈值的频繁序列模式,这里要求模式中任意两个相邻元素在序列中的出现都要满足用户自定义的间隔约束,并且模式在序列中的出现要满足one-off条件。在解决该问题上,已有算法M-OneOffMine在计算模式的支持度时,只考虑模式的每个字符在序列中的首次出现,导致计算的模式支持度远小于其真实支持度,以致许多频繁的模式没有被挖掘出来。为此,设计了一个有效的带有间隔约束的多序列模式挖掘算法--MMSP算法：首先,通过采用二维表保存模式的候选位置;然后,根据候选位置采用最左最优的思想选择匹配位置。通过生物DNA序列进行实验,多序列中元素序列数目不变而序列长度变化时,MMSP挖掘出的频繁模式总数是同类算法M-OneOffMine的3.23倍;在元素序列个数变化时,MMSP挖掘出的频繁模式个数平均是M-OneOffMine的4.11倍;这两种情况下MMSP都有更好的时间性能。在模式长度变化时,MMSP挖掘出的频繁模式个数分别平均是M-OneOffMine的2.21倍和MPP的5.24倍。同时还验证了M-OneOffMine挖掘到的模式是MMSP挖掘到的频繁的子集。实验结果表明,MMSP算法不仅可以挖掘到更多的频繁模式,而且时间花费更少,更适合于实际的应用。     

Mining sequential patterns with regular expression constraints

Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. We propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT-Sequential Pattern mining with Regular expression consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like anti monotonicity) are integrated into the mining process     

Mining hybrid sequential patterns and sequential rules

The problem addressed in this paper is to discover the frequently occurred sequential patterns from databases. Basically, the existing studies on finding sequential patterns can be roughly classified into two main categories. In the first category, the discovered patterns are continuous patterns, where all the elements in the pattern appear in consecutive positions in transactions. The second category is to mine discontinuous patterns, where the adjacent elements in the pattern need not appear consecutively in transactions. Although there are many researches on finding either kind of patterns, no previous researches can find both of them. Neither can they find the discontinuous patterns formed of several continuous sub-patterns. Therefore, we define a new kind of patterns, called hybrid pattern, which is the combination of continuous patterns and discontinuous patterns. In this paper, two algorithms are developed to mine hybrid patterns, where the first algorithm is easy but slow while the second complicated but much faster than the first one. Finally, the simulation result shows that our second algorithm is as fast as the currently best algorithm for mining sequential patterns.     

Mining non-redundant time-gap sequential patterns

Mining sequential patterns is to discover sequential purchasing behaviors for most of the customers from a large amount of customer transactions. An example of such a pattern is that most of the customers purchased item B after purchasing item A, and then they purchased item C after using item B. The manager can use this information to promote item B and item C when a customer purchased item A and item B, respectively. However, the manager cannot know what time the customers will need these products if we only discover the sequential patterns without any extra information. In this paper, we develop a new algorithm to discover not only the sequential patterns but also the time interval between any two items in the pattern. We call this information the time-gap sequential patterns. An example of time-gap sequential pattern is that most of the customers purchased item A, and then they bought item B after m to n days, and then after p to q days, they bought item C. When a customer bought item A, the information about item B can be sent to this customer after m to n days, that is, we can provide the product information in which the customer is interested on the appropriate date.     

基于加权有向图的权频繁模式挖掘算法

为解决加权图遍历模式的挖掘问题,提出了一种从加权有向图中挖掘加权频繁模式算法.在该算法中,利用图全局拓扑结构和顶点权值信息评估遍历模式的权支持度,从而将剪枝问题转化成模式可扩展性问题,再利用可扩展模式产生候选模式集.本算法把图,顶点权值融合进来,提高了挖掘结果的准确度.实验结果表明,该算法可以有效地进行基于加权向图的权频繁模式挖掘.     

去冗余Top-k对比序列模式挖掘

对比序列模式可以用来表征不同类别数据集之间的差异。在生物信息、物流管理、电子商务等领域,对比序列模式有着广泛的应用。Top-k对比序列模式挖掘的目标是发现数据集中对比度最高的前k个序列模式。在Top-k对比序列模式挖掘中,可能挖掘出冗余的序列模式。目前,虽然有Top-k对比序列模式发现算法被提出,但这些算法并未考虑冗余序列模式的问题。为此,本文提出了基于广度优先生成树的去冗余Top-k对比序列模式挖掘算法BFM（breadth-first miner）。使用BFM算法可以有效地解决冗余问题,得到去冗余的Top-k对比序列模式。在BFM算法的基础上,提出了性能更好的算法PBFM（pruning breadth-first miner）。通过在真实数据集上的实验分析与对比 ,验证了本文算法的有效性。     

Mining sequential patterns from probabilistic databases

Knowledge and Information Systems - This paper considers the problem of sequential pattern mining (SPM) in probabilistic databases. Specifically, we consider SPM in situations where there is...     

大型时态数据库中的Burst模式挖掘

首先分析了挖掘整个大型时态数据库时可能存在的两个问题,提出了解决的一种新方法。该方法采用“先分后合”的思想：先将大型数据库划分成多个小型数据集,接着对这些数据集进行四次裁剪后再进行综合评价,最后挖掘出潜在的Burst 模式。实验结果表明,该方法准确有效。挖掘出的Burst模式给公司决策者在制定决策的时候提供参考帮助和支持。