Assessment of data quality in accounting data with association rules

Business rules are an effective way to control data quality. Business experts can directly enter the rules into appropriate software without error prone communication with programmers. However, not all business situations and possible data quality problems can be considered in advance. In situations where business rules have not been defined yet, patterns of data handling may arise in practice. We employ data mining to accounting transactions in order to discover such patterns. The discovered patterns are represented in form of association rules. Then, deviations from discovered patterns can be marked as potential data quality violations that need to be examined by humans. Data quality breaches can be expensive but manual examination of many transactions is also expensive. Therefore, the goal is to find a balance between marking too many and too few transactions as being potentially erroneous. We apply appropriate procedures to evaluate the classification accuracy of developed association rules and support the decision on the number of deviations to be manually examined based on economic principles.     

基于关联规则的Web文档分类

在现有的Web文档分类器中,有的分类器产生比较精确的分类结果,有的分类器产生更易解释的分类模型,但还没有分类器可以将两个方面的优点结合起来.有鉴于此,论文提出一种基于关联规则的Web文档分类方法.该方法采用事务概念,主要考虑两方面的问题：①在文档训练集中发现最优的词条关联规则;②用这些规则构建一个Web文档分类器.试验表明该分类器性能良好,训练速度快,产生的规则易于被人理解,而且容易更新和调整.     

Using semantic techniques to access web data

Nowadays, people frequently use different keyword-based web search engines to find the information they need on the web. However, many words are polysemous and, when these words are used to query a search engine, its output usually includes links to web pages referring to their different meanings. Besides, results with different meanings are mixed up, which makes the task of finding the relevant information difficult for the users, especially if the user-intended meanings behind the input keywords are not among the most popular on the web.     

Novel measurement for mining effective association rules

Mining association rules are widely studied in data mining society. In this paper, we analyze the measure method of support–confidence framework for mining association rules, from which we find it tends to mine many redundant or unrelated rules besides the interesting ones. In order to ameliorate the criterion, we propose a new method of match as the substitution of confidence. We analyze in detail the property of the proposed measurement. Experimental results show that the generated rules by the improved method reveal high correlation between the antecedent and the consequent when the rules were compared with that produced by the support–confidence framework. Furthermore, the improved method decreases the generation of redundant rules.     

关联规则衡量标准的研究

关联规则采掘是数据采掘中重要的研究课题。针对当前关联规则采掘中可能产生许多无效关联规则的问题，分析其原因，提出在衡量标准中增加有效度，并给出了有效度的定义。根据有效度的大小，将关联规则分为正关联规则、无效关联规则、负关联规则，提出了新衡量标准采相关联规则的算法，并用Visual FoxPro进行了试验。实验表明，新方法能明显减少无效关联规则的数目。     

Mining association rules in very large clustered domains

Emerging applications introduce the requirement for novel association-rule mining algorithms that will be scalable not only with respect to the number of records (number of rows) but also with respect to the domain's size (number of columns). In this paper, we focus on the cases where the items of a large domain correlate with each other in a way that small worlds are formed, that is, the domain is clustered into groups with a large number of intra-group and a small number of inter-group correlations. This property appears in several real-world cases, e.g., in bioinformatics, e-commerce applications, and bibliographic analysis, and can help to significantly prune the search space so as to perform efficient association-rule mining. We develop an algorithm that partitions the domain of items according to their correlations and we describe a mining algorithm that carefully combines partitions to improve the efficiency. Our experiments show the superiority of the proposed method against existing algorithms, and that it overcomes the problems (e.g., increase in CPU cost and possible I/O thrashing) caused by existing algorithms due to the combination of a large domain and a large number of records.     

Mining fuzzy association rules for classification problems

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

Mining high coherent association rules with consideration of support measure

Data mining has been studied for a long time. Its goal is to help market managers find relationships among items from large databases and thus increase sales volume. Association-rule mining is one of the well known and commonly used techniques for this purpose. The Apriori algorithm is an important method for such a task. Based on the Apriori algorithm, lots of mining approaches have been proposed for diverse applications. Many of these data mining approaches focus on positive association rules such as “if milk is bought, then cookies are bought”. Such rules may, however, be misleading since there may be customers that buy milk and not buy cookies. This paper thus takes the properties of propositional logic into consideration and proposes an algorithm for mining highly coherent rules. The derived association rules are expected to be more meanful and reliable for business. Experiments on two datasets are also made to show the performance of the proposed approach.     

Using association rules to mine for strong approximate dependencies

In this paper we deal with the problem of mining for approximate dependencies (AD) in relational databases. We introduce a definition of AD based on the concept of association rule, by means of suitable definitions of the concepts of item and transaction. This definition allow us to measure both the accuracy and support of an AD. We provide an interpretation of the new measures based on the complexity of the theory (set of rules) that describes the dependence, and we employ this interpretation to compare the new measures with existing ones. A methodology to adapt existing association rule mining algorithms to the task of discovering ADs is introduced. The adapted algorithms obtain the set of ADs that hold in a relation with accuracy and support greater than user-defined thresholds. The experiments we have performed show that our approach performs reasonably well over large databases with real-world data.     

Online mining of fuzzy multidimensional weighted association rules

This paper addresses the integration of fuzziness with On-Line Analytical Processing (OLAP) based association rules mining. It contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube for knowledge discovery. A data cube is mainly constructed to provide users with the flexibility to view data from different perspectives as some dimensions of the cube contain multiple levels of abstraction. The first step of the process described in this paper involves introducing fuzzy data cube as a remedy to the problem of handling quantitative values of dimensional attributes in a cube. This facilitates the online mining of fuzzy association rules at different levels within the constructed fuzzy data cube. Then, we investigate combining the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules from the constructed fuzzy data cube. For this purpose, three different methods are introduced for single dimension, multidimensional and hybrid (integrates the other two methods) fuzzy weighted association rules mining. Each of the three methods utilizes a fuzzy data cube constructed to suite the particular method. To the best of our knowledge, this is the first effort in this direction. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results obtained for each of the three methods on a synthetic dataset and on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach. OLAP is one of the most popular tools for on-line, fast and effective multidimensional data analysis. In the OLAP framework, data is mainly stored in data hypercubes (simply called cubes).     

Elicitation of classification rules by fuzzy data mining

Data mining techniques can be used to find potentially useful patterns from data and to ease the knowledge acquisition bottleneck in building prototype rule-based systems. Based on the partition methods presented in simple-fuzzy-partition-based method (SFPBM) proposed by Hu et al. (Comput. Ind. Eng. 43(4) (2002) 735), the aim of this paper is to propose a new fuzzy data mining technique consisting of two phases to find fuzzy if–then rules for classification problems: one to find frequent fuzzy grids by using a pre-specified simple fuzzy partition method to divide each quantitative attribute, and the other to generate fuzzy classification rules from frequent fuzzy grids. To improve the classification performance of the proposed method, we specially incorporate adaptive rules proposed by Nozaki et al. (IEEE Trans. Fuzzy Syst. 4(3) (1996) 238) into our methods to adjust the confidence of each classification rule. For classification generalization ability, the simulation results from the iris data demonstrate that the proposed method may effectively derive fuzzy classification rules from training samples.     

An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization

Association rules are one of the most frequently used tools for finding relationships between different attributes in a database. There are various techniques for obtaining these rules, the most common of which are those which give categorical association rules. However, when we need to relate attributes which are numeric and discrete, we turn to methods which generate quantitative association rules, a far less studied method than the above. In addition, when the database is extremely large, many of these tools cannot be used. In this paper, we present an evolutionary tool for finding association rules in databases (both small and large) comprising quantitative and categorical attributes without the need for an a priori discretization of the domain of the numeric attributes. Finally, we evaluate the tool using both real and synthetic databases.     

Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria.     

基于免疫原理的模糊关联规则挖掘算法

提出一种基于免疫原理的人工免疫算法,用于模糊关联规则的挖掘.该算法通过借鉴生物免疫系统中的克隆选择原理来实施优化操作,它直接从给出的数据中,通过优化机制自动确定每个属性对应的模糊集合,使推导出的满足条件的模糊关联规则数目最多.将实际数据集和相关算法进行性能比较,实验结果表明了所提出算法的有效性.     

Multi-objective PSO algorithm for mining numerical association rules without a priori discretization

In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness.     

Association rules on significant rare data using second support

Association rule is one of the data mining techniques involved in discovering information that represents the association among data. Data in the database sometimes appear infrequent but highly associated with a specific data. This paper proposes a technique for significant rare data by introducing second support in discovering the association rules of such data. We show that the proposed approach provides better performance as compared to standard association rules techniques.     

Mining association rules using inverted hashing and pruning

In this paper, we propose a new algorithm named Inverted Hashing and Pruning (IHP) for mining association rules between items in transaction databases. The performance of the IHP algorithm was evaluated for various cases and compared with those of two well-known mining algorithms, Apriori algorithm [Proc. 20th VLDB Conf., 1994, pp. 487-499] and Direct Hashing and Pruning algorithm [IEEE Trans. on Knowledge Data Engrg. 9 (5) (1997) 813-825]. It has been shown that the IHP algorithm has better performance for databases with long transactions.     

Mining various semantic relationships from unstructured user-generated web data

With the emergence of Web 2.0, the amount of user-generated web data has sharply increased. Thus, many studies have proposed techniques to extract wisdom from these user-generated datasets. Some of these works have focused on extracting semantic relationships through the use of search logs or social annotations, but only hierarchical relationships have been considered. The goal of this paper is to detect various semantic relationships (hierarchical and non-hierarchical) between concepts using search logs and social annotations. The experimental results demonstrate that our proposed approach constructs adequate relationships.     

RMAIN: Association rules maintenance without reruns through data

Association rules are well recognised as a data mining tool for analysis of transactional data, currently going far beyond the early basket-based applications. A wide spectrum of methods for mining associations have been proposed up to date, including batch and incremental approaches. Most of the accurate incremental methods minimise, but do not completely eliminate reruns through processed data. In this paper we propose a new approximate algorithm RMAIN for incremental maintenance of association rules, which works repeatedly on subsequent portions of new transactions. After a portion has been analysed, the new rules are combined with the old ones, so that no reruns through the processed transactions are performed in the future. The resulting set of rules is kept similar to the one that would be achieved in a batch manner. Unlike other incremental methods, RMAIN is fully separated from a rule mining algorithm and this independence makes it highly general and flexible. Moreover, it operates on rules in their final form, ready for decision support, and not on intermediate representation (frequent itemsets), which requires further processing. These features make the RMAIN algorithm well suited for rule maintenance within knowledge bases of autonomous systems with strongly bounded resources and time for decision making. We evaluated the algorithm on synthetic and real datasets, achieving promising results with respect to either performance or quality of output rules.