An efficient data mining technique for discovering interesting association rules

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items bought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In addition, we extend our revised algorithm to maintain discovered association rules when incremental or decremental updates are made to the databases. Experimental results show the efficiency of our algorithms. The revised algorithm significantly improves over the original one on mining association rules. The algorithms for maintaining association rules are more efficient than re-running the mining algorithms for the whole updated database and outperform previously proposed algorithms that need multiple passes over the database.

One of the recent research topics in databases is Data Mining, to find, extract and mine the useful information from databases. In case of updating transactions in the database the already discovered knowledge may become invalid. So we need efficient knowledge management techniques for finding the updated knowledge from the database. There have been lot of research in data mining, but Knowledge Management in databases is not studied much. One of the data mining techniques is to find association rules from databases. But most of association rule algorithms find association rules from transactional databases. Our research is a further step of the Tree Based Association Rule Mining (TBAR) algorithm, used in relational databases for finding the association rules .In our approach of updating the already discovered knowledge; the proposed algorithm Association Rule Update (ARU), updates the already discovered association rules found through the TBAR algorithm. Our algorithm will be able to find incremental association rules from relational databases and efficiently manage the previously found knowledge.

Mining for association rules is considered an important data mining problem. Many different variations of this problem have been described in the literature. We introduce the problem of mining for negative associations. A naive approach to finding negative associations leads to a very large number of rules with low interest measures. We address this problem by combining previously discovered positive associations with domain knowledge to constrain the search space such that fewer but more interesting negative rules are mined. We describe an algorithm that efficiently finds all such negative associations and present the experimental results.

Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area.This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

Since its inception, mining frequent patterns have become an imperative issue in data mining. The main problem in this area is to find out the association rule that identifies the relationships among a set of items. But the most expensive step in association rule is finding frequent itemsets and hence it draw the attention of many important research. In this paper, we propose a novel tree structure, called GM(Generate and Merge)Tree, which is a combination of prefix based incremental mining using canonical ordering and batch incrementing techniques. Our approach makes the tree structure more compact, canonically ordered of nodes and avoids sequential incrementing of transactions. It also helps to give a scalable algorithm with minimum overheads of modifying the tree structure during update operations. This algorithm is especially expected to give better results in case of extremely large transaction database in a dynamic environment. The experimental work has been carried out on two large datasets. Test results show the efficiency and effectiveness of the proposed approach by outperforming the traditional FP-Tree, CanTree(Canonical-order Tree) and BIT(Batch Incremental Tree).

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master---slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the discovered models usually result in a very large set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist decision-making process. The previous handicap is heightened in presence of redundant rules in the final set. In this work a new definition of redundancy in association rules is proposed, based on user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows to find more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown reduction levels that exceed 90 percent of all generated rules, using prior knowledge always below ten percent. So, our method improves the efficiency of association rules mining and the exploitation of discovered association rules.

Association rule mining is one of the most popular pattern discovery methods used in data mining. Frequent pattern extraction is an essential step in association rule mining. Most of the proposed algorithms for extracting frequent patterns are based on the downward closure lemma concept utilizing the support and confidence framework. In this chapter we investigate an alternative method for mining frequent patterns in a transactional database. Self-Organizing Map (SOM) is an unsupervised neural network that effectively creates spatially organized internal representations of the features and abstractions detected in the input space. It is one of the most popular clustering techniques, and it reveals existing similarities in the input space by performing a topology-preserving mapping. These promising properties indicate that such a clustering technique can be used to detect frequent patterns in a top-down manner as opposed to the traditional approach that employs a bottom-up lattice search. Issues that are frequently raised when using clustering technique for the purpose of finding association rules are: (i) the completeness of association rule set, (ii) the support level for the rules generated, and (iii) the confidence level for the rules generated. We present some case studies analyzing the relationships between the SOM approach and the traditional association rule framework, and propose a way to constrain the clustering technique so that the traditional support constraint can be approximated. Throughout our experiments, we have demonstrated how a clustering approach can be used for discovering frequent patterns.

Data mining is an essential technology to extract patterns or knowledge from large repositories of data. Association rules in market basket database represent the shopping behavior of customers. The association information may reveal trade secrets. It must be hidden before publishing. Association rule hiding in privacy preserving data mining hides sensitive rules containing sensitive items. In this paper, a new method is proposed to detect the sensitive items for hiding sensitive association rules. This proposed method finds the frequent item sets and generates the association rules. It employs the concept of representative association rules to detect sensitive items.

A technique called data mining makes it easy to extract valuable insights from large databases. It is the procedure of acquiring knowledge ‎from information. This technical capability has generated much interest in society recently, especially in the information sector. Focusing on ‎the healthcare industry, which has significant implementation issues, this article emphasizes the need for software in data mining by offering ‎important backing for the development of effective and maintainable systems. Designing dependable systems, specifying both functional ‎and non-functional needs, creating efficient algorithms, guaranteeing sustainable development, improving performance, and facilitating inte-‎gration are important elements of software pertinent to this field. The study underlines that software underpins the execution of good and ‎efficient data mining techniques. Additionally, this research will examine uses of data mining in healthcare information as well as related ‎problems. This descriptive study examines the ideas, problems, and techniques applied in data mining, particularly in the healthcare sector‎.

In data mining, the association rules are used to search for the relations of items of the transactions database. Following the data collected and stored, it can find values through association rules, and assist manager to proceed marketing strategies and plan market framework. In this paper, we attempt to use fuzzy partition method and decide membership function of quantitative values of each transaction item. Also, from managers we can reflect the importance of items as linguistic terms, which are transformed as fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth is used to complete the process of data mining. The method above is expected to improve Apriori algorithm for its better efficiency of the whole association rules. An example is given to clearly illustrate the proposed approach. Finally, the experiment results are made to show the performance of the proposed methods.

It is very time-consuming to discover association rules from the mass of data, but not all the rules are interesting to the user, a lot of irrelevant information to the user's requirements may be generated when traditional mining methods are applied. In addition, most of the existing algorithms are for discovering one-dimensional association rules. Therefore, this paper defines a mining language which allows users to specify items of interest to the association rules, as well as the criteria (for example, support, confidence, etc.), and proposes a method based on rough set theory for multi-dimensional association rule mining methods, dynamically generate frequent item sets and multi-dimensional association rules, which can reduce the search space to generate frequent itemsets. Finally, an example is used to illustrate the algorithm and verify its feasibility and effectiveness.

Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not taken the changes in associations into consideration and this can result in severe degradation of performance, especially when the discovered association rules are used for classification (prediction). Although the mining of changes in associations is an important problem because it is common that we need to predict the future based on the historical data in the past, existing data mining algorithms are not developed for this task. In this paper, we introduce a new fuzzy data mining technique to discover changes in association rules over time. Our approach mines fuzzy rules to represent the changes in association rules. Based on the discovered fuzzy rules, our approach is able to predict how the association rules will change in the future. The experimental results on a real-life database have shown that our approach is very effective in mining and predicting changes in association rules over time.

Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently.