Flexible online association rule mining based on multidimensional pattern relations

Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide ad-hoc, query-driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. We then develop an online mining approach called three-phase online association rule mining (TOARM) based on this proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. The TOARM approach consists of three phases during which final sets of patterns satisfying various mining requests are found. It first selects and integrates related mining information in the multidimensional pattern relation, and then if necessary, re-processes itemsets without sufficient information against the underlying datasets. Some implementation considerations for the algorithm are also stated in detail. Experiments on homogeneous and heterogeneous datasets were made and the results show the effectiveness of the proposed approach.     

Mass classification method in mammograms using correlated association rule mining

In this paper, we present an efficient computer-aided mass classification method in digitized mammograms using Association rule mining, which performs benign–malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Association rule mining (ARM) exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Correlated association rule mining was proposed for classifying the marked regions into benign and malignant and 98.6% sensitivity and 97.4% specificity is achieved that is very much promising compare to the radiologist’s sensitivity 75%.     

基于激励的关联规则的挖掘

基于支持度的关联规则挖掘算法无法找到那些非频繁但效用很高的项集,基于效用的关联规则会漏掉那些效用不高但发生比较频繁、支持度和效用值的积（激励）很大的项集。提出了基于激励的关联规则挖掘问题及一种自下而上的挖掘算法HM-miner。激励综合了支持度与效用的优点,能同时度量项集的统计重要性和语义重要性。HM-miner利用激励的上界特性进行减枝,能有效挖掘高激励项集。     

Privacy-preserving collaborative association rule mining

This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among parties involved in a data mining task. We study how to share private or confidential data in the following scenario: multiple parties, each having a private data set, want to collaboratively conduct association rule mining without disclosing their private data to each other or any other parties. To tackle this demanding problem, we develop a secure protocol for multiple parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private.     

Preserving privacy in association rule mining with bloom filters

Privacy preserving association rule mining has been an active research area since recently. To this problem, there have been two different approaches—perturbation based and secure multiparty computation based. One drawback of the perturbation based approach is that it cannot always fully preserve individual’s privacy while achieving precision of mining results. The secure multiparty computation based approach works only for distributed environment and needs sophisticated protocols, which constrains its practical usage. In this paper, we propose a new approach for preserving privacy in association rule mining. The main idea is to use keyed Bloom filters to represent transactions as well as data items. The proposed approach can fully preserve privacy while maintaining the precision of mining results. The tradeoff between mining precision and storage requirement is investigated. We also propose δ-folding technique to further reduce the storage requirement without sacrificing mining precision and running time.     

语义驱动的关联规则挖掘算法研究

为了使传统的关联规则挖掘算法在结合到具体领域时具有更强的适应性,提出了DS-Apriori算法。该算法建立在语义本体的基础上,根据项集内部的语义相关度动态的确定该项集的最小支持度,并采用了项集语义相关度的增量计算方法。实验结果表明,DS-Apriori算法在很大程度上提高了关联规则挖掘算法的效率和效果。     

Rare association rule mining from incremental databases

Pattern Analysis and Applications - Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous...     

Query-based association rule mining supporting user perspective

Two parameters, namely support and confidence, in association rule mining, are used to arrange association rules in either increasing or decreasing order. These two parameters are assigned values by counting the number of transactions satisfying the rule without considering user perspective. Hence, an association rule, with low values of support and confidence, but meaningful to the user, does not receive the same importance as is perceived by the user. Reflecting user perspective is of paramount importance in light of improving user satisfaction for a given recommendation system. In this paper, we propose a model and an algorithm to extract association rules, meaningful to a user, with an ad-hoc support and confidence by allowing the user to specify the importance of each transaction. In addition, we apply the characteristics of a concept lattice, a core data structure of Formal Concept Analysis (FCA) to reflect subsumption relation of association rules when assigning the priority to each rule. Finally, we describe experiment results to verify the potential and efficiency of the proposed method.     

关联规则挖掘Apriori算法的改进

在分析研究关联规则挖掘Apriori算法及其若干改进算法的基础上,对Apriori算法做了进一步地改进,提出一种基于条件判断的新思想.改进后的算法根据条件采用了事务压缩与候选项压缩的相结合的方式,减小了不必要的开销,从而提高了挖掘速度.     

An information-theoretic approach to quantitative association rule mining

Quantitative association rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike boolean association rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the high-confidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.     

Auditing file system permissions using association rule mining

Identifying irregular file system permissions in large, multi-user systems is challenging due to the complexity of gaining structural understanding from large volumes of permission information. This challenge is exacerbated when file systems permissions are allocated in an ad-hoc manner when new access rights are required, and when access rights become redundant as users change job roles or terminate employment. These factors make it challenging to identify what can be classed as an irregular file system permission, as well as identifying if they are irregular and exposing a vulnerability. The current way of finding such irregularities is by performing an exhaustive audit of the permission distribution; however, this requires expert knowledge and a significant amount of time. In this paper a novel method of modelling file system permissions which can be used by association rule mining techniques to identify irregular permissions is presented. This results in the creation of object-centric model as a by-product. This technique is then implemented and tested on Microsoft’s New Technology File System permissions (NTFS). Empirical observations are derived by making comparisons with expert knowledge to determine the effectiveness of the proposed technique on five diverse real-world directory structures extracted from different organisations. The results demonstrate that the technique is able to correctly identify irregularities with an average accuracy rate of 91%, minimising the reliance on expert knowledge. Experiments are also performed on synthetic directory structures which demonstrate an accuracy rate of 95% when the number of irregular permissions constitutes 1% of the total number. This is a significant contribution as it creates the possibility of identifying vulnerabilities without prior knowledge of how to file systems permissions are implemented within a directory structure.     

Product portfolio identification based on association rule mining

It has been well recognized that product portfolio planning has far-reaching impact on the company's business success in competition. In general, product portfolio planning involves two main stages, namely portfolio identification and portfolio evaluation and selection. The former aims to capture and understand customer needs effectively and accordingly to transform them into specifications of product offerings. The latter concerns how to determine an optimal configuration of these identified offerings with the objective of achieving best profit performance. Current research and industrial practice have mainly focused on the economic justification of a given product portfolio, whereas the portfolio identification issue has been received only limited attention. This article intends to develop explicit decision support to improve product portfolio identification by efficient knowledge discovery from past sales and product records. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with requirement analysis enacted among customers, marketing folks, and designers. An association rule mining system (ARMS) is proposed for effective product portfolio identification. Based on a scrutiny into the product definition process, the article studies the fundamental issues underlying product portfolio identification. The ARMS differentiates the customer needs from functional requirements involved in the respective customer and functional domains. Product portfolio identification entails the identification of functional requirement clusters in conjunction with the mappings from customer needs to these clusters. While clusters of functional requirements are identified based on fuzzy clustering analysis, the mapping mechanism between the customer and functional domains is incarnated in association rules. The ARMS architecture and implementation issues are discussed in detail. An application of the proposed methodology and system in a consumer electronics company to generate a vibration motor portfolio for mobile phones is also presented.     

Implicit feature identification via hybrid association rule mining

In sentiment analysis, a finer-grained opinion mining method not only focuses on the view of the product itself, but also focuses on product features, which can be a component or attribute of the product. Previous related research mainly relied on explicit features but ignored implicit features. However, the implicit features, which are implied by some words or phrases, are so significant that they can express the users’ opinion and help us to better understand the users’ comments. It is a big challenge to detect these implicit features in Chinese product reviews, due to the complexity of Chinese. This paper is mainly centered on implicit features identification in Chinese product reviews. A novel hybrid association rule mining method is proposed for this task. The core idea of this approach is mining as many association rules as possible via several complementary algorithms. Firstly, we extract candidate feature indicators based word segmentation, part-of-speech (POS) tagging and feature clustering, then compute the co-occurrence degree between the candidate feature indicators and the feature words using five collocation extraction algorithms. Each indicator and the corresponding feature word constitute a rule (feature indicator → feature word). The best rules in five different rule sets are chosen as the basic rules. Next, three methods are proposed to mine some possible reasonable rules from the lower co-occurrence feature indicators and non indicator words. Finally, the latest rules are used to identify implicit features and the results are compared with the previous. Experiment results demonstrate that our proposed approach is competent at the task, especially via using several expanding methods. The recall is effectively improved, suggesting that the shortcomings of the basic rules have been overcome to certain extent. Besides those high co-occurrence degree indicators, the final rules also contain uncommon rules.     

基于FP-Tree的约束关联规则挖掘算法

针对构建FP-Tree时存在的大量内存消耗问题,提出了CCFP(constraint clip FP-tree)算法,该算法利用有项和缺项约束对事务数据库进行修剪后构造简化的FP-Tree,经再一次扫描后得到关联规则.实验结果表明:该算法较一般的FP-Tree算法能节省大量的内存空间,同时,运行效率也略有提高.     

关联规则挖掘中一种实视图选择策略

关联规则挖掘是数据挖掘问题中一个典型任务。其挖掘响应时间是数据挖掘系统中重要的问题之一。为了高效解决这一问题,给出了关联规则实视图的概念以及相应的代价模型;提出了针对数据挖掘环境的实视图选择算法,以便在存储空间约束的条件下,取得较好的查询性能。实验结果表明,该算法能有效地选取实视图,从而大大提高关联规则挖掘算法的效率。     

分布式抽样关联规则挖掘算法的研究

介绍了假日旅游信息数据挖掘的概念,提出了一种改进的分布式抽样关联规则挖掘算法DS-ARM,给出了算法的实现过程,并对算法性能进行了测试,利用DS-ARM算法对假日旅游者在目的地的旅游行为模式进行了研究。     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

一种旅行数据约束关联规则挖掘算法

随着旅游业的发展,从海量旅行数据中挖掘旅客类型和环境因素之间内在的、隐含的相关性,是分析旅游市场状况、预测对相关行业影响的一种有效方法。结合旅行数据特点,并针对现有约束方法的局限性,提出一种基于关系延展路径约束的关联规则并行挖掘算法。该算法有效结合MapReduce并行机制,在关系延展路径约束下生成事务集,提升后续并行效率;同时利用并行方法改进Apriori算法的逐层搜索,带来“二次”效率提升,从而更好更快地把握旅游业发展动态,调整旅游业宏观政策。