Mining constrained frequent itemsets from distributed uncertain data

Nowadays, high volumes of massive data can be generated from various sources (e.g., sensor data from environmental surveillance). Many existing distributed frequent itemset mining algorithms do not allow users to express the itemsets to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous itemsets that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, the data are often riddled with uncertainty. These call for both constrained mining and uncertain data mining. In this journal article, we propose a data-intensive computer system for tree-based mining of frequent itemsets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data.     

Weighted frequent itemset mining over uncertain databases

Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete. To address these issues and mine more useful and meaningful knowledge, the problems of weighted and uncertain itemset mining have been respectively proposed, where a user may respectively assign weights to items to specify their relative importance, and specify existential probabilities to represent uncertainty in transactions. However, no work has addressed both of these issues at the same time. In this paper, we address this important research problem by designing a new type of patterns named high expected weighted itemset (HEWI) and the HEWI-Uapriori algorithm to efficiently discover HEWIs. The HEWI-Uapriori finds HEWIs using an Apriori-like two-phase approach. The algorithm introduces a property named high upper-bound expected weighted downward closure (HUBEWDC) to early prune the search space and unpromising itemsets. Substantial experiments on real-life and synthetic datasets are conducted to evaluate the performance of the proposed algorithm in terms of runtime, memory consumption, and number of patterns found. Results show that the proposed algorithm has excellent performance and scalability compared with traditional methods for weighted-itemset mining and uncertain itemset mining.     

Discovering Transitional Patterns and Their Significant Milestones in Transaction Databases

A transaction database usually consists of a set of time-stamped transactions. Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms (such as Apriori and FP-growth) do not consider the time stamps associated with the transactions. In this paper, we extend the existing frequent pattern mining framework to take into account the time stamp of each transaction and discover patterns whose frequency dramatically changes over time. We define a new type of patterns, called transitional patterns, to capture the dynamic behavior of frequent patterns in a transaction database. Transitional patterns include both positive and negative transitional patterns. Their frequencies increase/decrease dramatically at some time points of a transaction database. We introduce the concept of significant milestones for a transitional pattern, which are time points at which the frequency of the pattern changes most significantly. Moreover, we develop an algorithm to mine from a transaction database the set of transitional patterns along with their significant milestones. Our experimental studies on real-world databases illustrate that mining positive and negative transitional patterns is highly promising as a practical and useful approach for discovering novel and interesting knowledge from large databases.     

A new mining approach for uncertain databases using CUFP trees

In the past, many algorithms have been proposed to mine frequent itemsets from transactional databases, in which the presence or absence of items in transactions was certainly known. In some applications, items may also be uncertain in transactions with their existential probabilities ranging from 0 to 1 in the uncertain dataset. Apparently, the processing in uncertain datasets is quite different from those in certain datasets. The UF-tree algorithm was proposed to construct the UF-tree structure from an uncertain dataset and mine frequent itemsets from the tree. In the UF-tree construction process, however, only the same items with the same existential probabilities in transactions were merged together in the tree, thus causing many redundant nodes in the tree. In this paper, a new tree structure called the compressed uncertain frequent-pattern tree (CUFP tree) is designed to efficiently keep the related information in the mining process. In the CUFP tree, the same items will be merged in a branch of the tree even when the existential probabilities in transactions are not the same. A mining algorithm called the CUFP-mine algorithm is then proposed based on the tree structure to find uncertain frequent patterns. Experimental results show that the proposed approach has a better performance than UF-tree algorithm both in the execution time and in the number of tree nodes.     

Looking into the seeds of time: Discovering temporal patterns in large transaction sets

This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets. A model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them. A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent. The model is general in that (i) no constraints are placed on the interesting patterns given by the users, and (ii) two measures—inclusiveness and exclusiveness—are used to capture how well the temporal patterns match the time points given by the discovered itemsets. Intuitively, these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p, but not at time points not in p. Using these two measures, one is able to model many temporal data mining problems appeared in the literature, as well as those that have not been studied. By exploiting the relationship within and between itemset space and pattern space simultaneously, a series of pruning techniques are developed to speed up the mining process. Experiments show that these pruning techniques allow one to obtain performance benefits up to 100 times over a direct extension of non-temporal data mining algorithms.     

A constraint-based querying system for exploratory pattern discovery

In this article we present ConQueSt, a constraint-based querying system able to support the intrinsically exploratory (i.e., human-guided, interactive and iterative) nature of pattern discovery. Following the inductive database vision, our framework provides users with an expressive constraint-based query language, which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to reduce the cost of pattern mining computation. ConQueSt is a comprehensive mining system that can access real-world relational databases from which to extract data. Through the interaction with a friendly graphical user interface (GUI), the user can define complex mining queries by means of few clicks. After a pre-processing step, mining queries are answered by an efficient and robust pattern mining engine which entails the state-of-the-art of data and search space reduction techniques. Resulting patterns are then presented to the user in a pattern browsing window, and possibly stored back in the underlying database as relations.     

从图数据库中挖掘频繁跳跃模式

很多频繁子图挖掘算法已被提出.然而,这些算法产生的频繁子图数量太多而不能被用户有效地利用.为此,提出了一个新的研究问题:挖掘图数据库中的频繁跳跃模式.挖掘频繁跳跃模式既可以大幅度地减少输出模式的数量,又能使有意义的图模式保留在挖掘结果中.此外,跳跃模式还具有抗噪声干扰能力强等优点.然而,由于跳跃模式不具有反单调性质,挖掘它们非常具有挑战性.通过研究跳跃模式自身的特性,提出了两种新的裁剪技术:基于内扩展的裁剪和基于外扩展的裁剪.在此基础上又给出了一种高效的挖掘算法GraphJP(an algorithm for mining jump patterns from graph databases).另外,还严格证明了裁剪技术和算法GraphJP的正确性.实验结果表明,所提出的裁剪技术能够有效地裁剪图模式搜索空间,算法GraphJP是高效、可扩展的.     

基于ACV约束的序列模式挖掘算法

针对变值数据环境下的序列模式挖掘问题进行研究,提出一种针对变值数据的约束(ACV约束),用于表达用户在变值数据环境下对序列模式聚集特征的要求。在此基础上,提出一种基于ACV约束的序列模式挖掘算法,利用ACV约束的性质有效削减搜索空间。在用IBM数据生成器产生的序列数据以及真实数据上的实验结果表明,该算法能够有效利用ACV约束对无用的候选序列模式进行剪枝,减少冗余的搜索空间并提高挖掘效率。     

基于时效性和相关性约束的周期模式挖掘

传统周期模式挖掘忽略了模式本身的相关性和时效性,导致获取到一些实用价值有限的弱相关且时效性较低的模式。因此,提出了新颖的基于时效性和相关性约束的周期模式挖掘方法(correlation and recency periodic frequent pattern-breadth first search, CRPFP-BFS)和(correlation and recency periodic frequent pattern-depth first search, CRPFP-DFS)。将给定的数据库压缩到一个列式结构的列表CRPFP-List中,CRPFP-BFS和CRPFP-DFS分别采用广度优先和深度优先搜索方式递归地进行挖掘,同时利用支持度、周期、时效性以及相关性剪枝策略减少搜索空间,以有效地发现相关时效周期模式。与当前最先进算法在密集数据集和稀疏数据集上进行对比实验,结果表明CRPFP-BFS和CRPFP-DFS具有较低的内存占用和更高的运行效率,并且具有良好的可扩展性,其中CRPFP-DFS适合于内存要求严格的情况,CRPFP-BFS在长事务稀疏数据集下的运行效率更高。     

An efficient algorithm for mining top-<Emphasis Type="Italic">k</Emphasis> on-shelf high utility itemsets

High on-shelf utility itemset (HOU) mining is an emerging data mining task which consists of discovering sets of items generating a high profit in transaction databases. The task of HOU mining is more difficult than traditional high utility itemset (HUI) mining, because it also considers the shelf time of items, and items having negative unit profits. HOU mining can be used to discover more useful and interesting patterns in real-life applications than traditional HUI mining. Several algorithms have been proposed for this task. However, a major drawback of these algorithms is that it is difficult for users to find a suitable value for the minimum utility threshold parameter. If the threshold is set too high, not enough patterns are found. And if the threshold is set too low, too many patterns will be found and the algorithm may use an excessive amount of time and memory. To address this issue, we propose to address the problem of top-k on-shelf high utility itemset mining, where the user directly specifies k, the desired number of patterns to be output instead of specifying a minimum utility threshold value. An efficient algorithm named KOSHU (fast top-K on-shelf high utility itemset miner) is proposed to mine the top-k HOUs efficiently, while considering on-shelf time periods of items, and items having positive and/or negative unit profits. KOSHU introduces three novel strategies, named efficient estimated co-occurrence maximum period rate pruning, period utility pruning and concurrence existing of a pair 2-itemset pruning to reduce the search space. KOSHU also incorporates several novel optimizations and a faster method for constructing utility-lists. An extensive performance study on real-life and synthetic datasets shows that the proposed algorithm is efficient both in terms of runtime and memory consumption and has excellent scalability.     

Mining association rules from quantitative data

Data-mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values, however, transactions with quantitative values are commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting interesting knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the apriori mining algorithm to find interesting fuzzy association rules in given transaction data sets. Experiments with student grades at I-Shou University were also made to verify the performance of the proposed algorithm.     

Effective periodic pattern mining in time series databases

The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequentially one after another in exact positions within this pattern. Though there are certain scenarios where a pattern can be flexible, that is, it may be interesting and can be mined by neglecting any number of unimportant events in between important events with variable length of the pattern. Moreover, existing algorithms can detect only specific type of periodicity in various time series databases and require the interaction from user to determine periodicity. In this paper, we have proposed an algorithm for the periodic pattern mining in time series databases which does not rely on the user for the period value or period type of the pattern and can detect all types of periodic patterns at the same time, indeed these flexibilities are missing in existing algorithms. The proposed algorithm facilitates the user to generate different kinds of patterns by skipping intermediate events in a time series database and find out the periodicity of the patterns within the database. It is an improvement over the generating pattern using suffix tree, because suffix tree based algorithms have weakness in this particular area of pattern generation. Comparing with the existing algorithms, the proposed algorithm improves generating different kinds of interesting patterns and detects whether the generated pattern is periodic or not. We have tested the performance of our algorithm on both synthetic and real life data from different domains and found a large number of interesting event sequences which were missing in existing algorithms and the proposed algorithm was efficient enough in generating and detecting periodicity of flexible patterns on both types of data.     

基于概率衰减窗口模型的不确定数据流频繁模式挖掘

考虑到不确定数据流的不确定性,设计了一种新的概率频繁模式树PFP-tree和基于该树的概率频繁模式挖掘方法PFP-growth.PFP-growth使用事务性不确定数据流及概率衰减窗口模型,通过计算各概率数据项的期望支持度以发现概率频繁模式,其主要特点有:考虑到窗口内不同时间到达数据项的贡献度不同,采用概率衰减窗口模型计算期望支持度,以提高模式挖掘准确度;设置数据项索引表和事务索引表,以加快频繁模式树检索速度;通过剪枝删除不可能成为频繁模式的结点,以降低模式树的存储及检索开销;对每个结点都设立一个事务概率信息链表,以支持数据项在不同事务中具有不同概率的情形.实验结果表明,PFP-growth在保证挖掘模式准确度的前提下,在处理时间和内存空间等方面都具有较好的性能.     

基于最大值约束的模糊关联规则挖掘

数据挖掘是从数据库中发现潜在有用知识或者感兴趣模式的过程。在数据挖掘领域中主要集中于单一支持度下的关联规则挖掘,在事务数据库中发现项目之间的关联性,而在实际应用中,项目可以有不同的最小支持度,不同的项目可能具有不同的标准去判断其重要性,因此提出一个在最大值支持度约束下,发现有用的模糊关联规则挖掘算法,在该约束下,利用逐层搜索的迭代方法发现频繁项目集,通过实例证明了该挖掘算法是易于理解和有意义的,具有很好的效率。     

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     

Construction and evaluation of structured association map for visual exploration of association rules

The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules.     

一种基于规则的离群挖掘算法

离群数据挖掘是指从大量数据中挖掘明显偏离、不满足一般行为模式的数据。现有的离群数据挖掘算法大多对密集的交易数据库缺乏有效的处理,文中提出了一种高效的基于规则的离群挖掘算法。该算法使用了多层最大离群支持度及最小离群兴趣度,计算1-离群条件集的幂集,并在数据结构中存储了交易标识符链表,使得扫描数据库的次数仅为一次,从而提高了挖掘的速度、效率且使得结果更具有决策意义。文中使用此算法对某一商场的部分销售数据库进行了实验,结果表明该算法能有效、迅速地发现密集数据库中的离群数据。     

Uncertain Distance-Based Range Queries over Uncertain Moving Objects

Distance-based range search is crucial in many real applications. In particular, given a database and a query issuer, a distance-based range search retrieves all the objects in the database whose distances from the query issuer are less than or equal to a given threshold. Often, due to the accuracy of positioning devices, updating protocols or characteristics of applications (for example, location privacy protection), data obtained from real world are imprecise or uncertain. Therefore, existing approaches over exact databases cannot be directly applied to the uncertain scenario. In this paper, we redefine the distance-based range query in the context of uncertain databases, namely the probabilistic uncertain distance-based range (PUDR) queries, which obtain objects with confidence guarantees. We categorize the topological relationships between uncertain objects and uncertain search ranges into six cases and present the probability evaluation in each case. It is verified by experiments that our approach outperform Monte-Carlo method utilized in most existing work in precision and time cost for uniform uncertainty distribution. This approach approximates the probabilities of objects following other practical uncertainty distribution, such as Gaussian distribution with acceptable errors. Since the retrieval of a PUDR query requires accessing all the objects in the databases, which is quite costly, we propose spatial pruning and probabilistic pruning techniques to reduce the search space. Two metrics, false positive rate and false negative rate are introduced to measure the qualities of query results. An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.     

Mining itemset utilities from transaction databases

The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent itemset only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a utility based itemset mining approach to overcome this limitation. The proposed approach permits users to quantify their preferences concerning the usefulness of itemsets using utility values. The usefulness of an itemset is characterized as a utility constraint. That is, an itemset is interesting to the user only if it satisfies a given utility constraint. We show that the pruning strategies used in previous itemset mining approaches cannot be applied to utility constraints. In response, we identify several mathematical properties of utility constraints. Then, two novel pruning strategies are designed. Two algorithms for utility based itemset mining are developed by incorporating these pruning strategies. The algorithms are evaluated by applying them to synthetic and real world databases. Experimental results show that the proposed algorithms are effective on the databases tested.     

Mining itemset utilities from transaction databases

The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent itemset only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a utility based itemset mining approach to overcome this limitation. The proposed approach permits users to quantify their preferences concerning the usefulness of itemsets using utility values. The usefulness of an itemset is characterized as a utility constraint. That is, an itemset is interesting to the user only if it satisfies a given utility constraint. We show that the pruning strategies used in previous itemset mining approaches cannot be applied to utility constraints. In response, we identify several mathematical properties of utility constraints. Then, two novel pruning strategies are designed. Two algorithms for utility based itemset mining are developed by incorporating these pruning strategies. The algorithms are evaluated by applying them to synthetic and real world databases. Experimental results show that the proposed algorithms are effective on the databases tested.