Efficient Remining of Generalized Association Rules Under Multiple Minimum Support Refinement

In business applications, there have been tremendous interests in analysing customers' repeated purchase behaviour. Recently, the concepts of periodic pattern and cyclic pattern are used to discover recurring patterns from customer sequence database. Toroslu (2003) proposed cyclic pattern mining, which considers a new parameter, named repetition support, into the mining process. In a customer sequence, the occurrence of a subsequence must satisfy single user-specified repetition minimum support. In real-life applications, however, different items may have different frequencies in the database. If all items are set to have the same minimum repetition support, it may cause rare item problem. To solve this problem, we include the concept of multiple minimum supports (MMS) to allow users to specify multiple minimum item repetition support (MIR) according to the natures of items. In this paper, we first redefine cyclic sequential patterns based on MIR and original form of customer minimum support. A new algorithm, rep-PrefixSpan, is developed to discover complete set of cyclic sequential patterns from sequence database. The experimental result shows that the proposed approach achieves more preferable findings than conventional cyclic pattern mining.

Efficient mining of generalized association rules with non-uniform minimum support

Generalized association rule mining using an efficient data structure

In knowledge mining, current trend is witnessing the emergence of a growing number of works towards defining "concise and lossless" representations. One main motivation behind is: tagging a unified framework for drastically reducing large sized sets of association rules. In this context, generic bases of association rules - whose backbone is the conjunction of the concepts of minimal generator (MG) and closed itemset (CI) - constituted so far irreducible compact nuclei of association rules. However, the inherent absence of a unique MG associated to a given CI offers an "ideal" gap towards a tougher redundancy removal even from generic bases of association rules. In this paper, we adopt the succinct system of minimal generators (SSMG), as newly redefined in [1], to be an exact representation of the MG set. Then, we incorporate the SSMG into the framework of generic bases to only maintain the succinct generic association rules. After that, we give a thorough formal study of the related inference mechanisms allowing to derive all redundant association rules starting from succinct ones. Finally, an experimental study shows that our approach makes it possible to eliminate without information loss an important number of redundant generic association rules and thus, to only present succinct and informative ones to users.

The alarm correlation analysis based on multi-level fuzzy association rules mining is the cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, multi-level fuzzy association rules mining algorithms are proposed, and two strategies are adopted to set minimum support, which are multiple minimum supports and one minimum support. Simulations are carried out to the comparison of algorithms under the two strategies. Multi-level fuzzy association rules mining of alarms is effectively realized. The advantages and efficiency of algorithms are demonstrated by the experiments.

Multi-dimensional semantic clustering of large databases for association rule mining

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

Data mining aims to develop a new generation of tools to intelligently assist humans in analyzing mountains of data. Over the past few years, great progress has been made in both research and applications of data mining. Data mining systems have helped many businesses by exposing previously unknown patterns in their databases, which were used to improve profits, enhance customer services, and ultimately achieve a competitive advantage. In this paper, we present our unique data mining system DM-II (Data Mining Integration and Interestingness). DM-II is a PC-based system working in Windows 95/98/NT environment. Apart from the normal components of a data mining system, DM-II has a number of unique and advanced sub-systems. These sub-systems have been applied in many real-life applications, including education applications, insurance application, accident application, disease application, drug screening application, image classification, etc. Here, we focus on discussing the following sub-systems: CBA: CBA originally stands for Classification-Based on Associations [lo]. It has now been extended with a number of other advanced features. In all, CBA has the following capabilities: Building classifiers using association rules: Traditionally, association rule mining and classifier building are regarded as two different data mining tasks. CBA unifies the two tasks. It is able to ,generate association rules and builds a classifier using a special subset of the association rules. What is significant is that CBA, in general, produces more accurate classifiers compared to the state-ofthe-art classification system C4.5 [ 161. It also helps to solve some outstanding problems with the existing classification systems. Pruning and summarizing the discovered associations: One major problem with association

We explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., 1) lack of consideration of the exhibition period of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm progressive-partition-miner (abbreviated as PPM) to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2-itemset based on the intrinsic partitioning characteristics. Algorithm PPM is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. The feature that the number of candidate 2-itemsets generated by PPM is very close to the number of frequent 2-itemsets allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of PPM is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm PPM.

The problem of existing customer relationship management (CRM) system is not lack of information but the ability to differentiate useful information from chatter or even disinformation and also maximize the richness of these heterogeneous information sources. This paper describes an improved text mining approach for automatically extracting association rules from collections of textual documents. It discovers association rules from keyword features extracted from the documents. The main contributions of the technique are that, in selecting the most discriminative keywords for use in association rules generation, the system combines syntactic and semantic relevance into its Information Retrieval Scheme which is integrated with XML technology. Experiments carried out revealed that the extracted association rules contain important features which form a worthy platform for making effective decisions as regards customer relationship management. The performance of the improved text mining approach is compared with existing system that uses the GARW algorithm to reveal a significant reduction in the large itemsets, leading to reduction in rules generated to more interesting ones due to the semantic analysis component being introduced. Also, it has brought about reduction of the execution time, compared to the GARW algorithm.

Association rules mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining association rules are built on the binary attributes databases, which has three limitations. Firstly, it can not concern quantitative attributes; secondly, only the positive association rules are discovered; thirdly, it treat each item with the same frequency although different item may have different frequency. In this paper, we put forward a discovery algorithm for mining positive and negative fuzzy association rules to resolve these three limitations.

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention.Association Rule Mining (ARM) is a well known and most popular technique of Data Mining.It identifies itemsets from the dataset which appears frequently and generates association rules.This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets.In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue.Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining.But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain.High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold.Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit.Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest.In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases.This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al.The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.

Generally, the study of association mining is majority concentrate on how to find out the frequent item set and attempt to infer the relationship between them. But very few studies deliberate about the two notable issues. The one is the huge number of association rules, which easily caused the decision maker to get lost in it. The other is the general association rules, which just imply the relationship with “AND” logic between items, but not imply the relation with “OR” and “XOR” logic between items. In this paper, we apply Karnaugh Map (K-Map) principle to find out ensemble association rules by experiment transaction data, it names ‘ARKM’. The experiment result shows that the ARKM approach which not only provides computational efficiency to obtain simplified and usable rules but also manifest adaptive to the decision maker.

Machine learning plays an important role to develop smart cities by gathering real time information using several state of the art algorithms. In the recent past, association rule mining plays an important role in the discovery of accurate information from databases to satisfy the need of real time applications in smart cities ranging from healthcare to intelligent transport systems. It is used in various applications for decision making, detection and prediction etc. because of its robustness to derive associations among various attributes of datasets. This technique seems to be simple in case of categorical data but becomes quite complex in case of numeric data. In this work, we have mainly concentrated on the problem of generating association rules from numeric data in an efficient way. For accomplishing this task we have taken genetic algorithm as the base of the solution to this problem. Genetic algorithm is selected for this task because of its nature of self-improving and ability to handle large solution set. Here we have proposed genetic algorithm based association rule mining algorithm which generates random association rules on the basis of general property of datasets. The generated rule set is improved at each run of algorithm and filtered for more and more interesting and accurate rules.

SummaryOne of the most important challenges of African countries is the effective use of information and communication technologies and its generalization in the different sectors (education, economic, political, and so on). This will have a great impact on different aspects of society and economic activities by making everyday procedures easier and more efficient. In this same context, we are interested in this article by the proposition of a decision support framework based on the use of data mining (DM) techniques. Using DM in e‐government is the process of translating data to appropriate knowledge which can be useful for decision‐making. We propose a framework whose goal is the generation of association rules (ARs) for better decision‐making. This framework includes two approaches: (1) the first approach applies different DM algorithms and (2) the second approach optimizes the first one by considering two different metaheuristics: the Genetic algorithm and the Cuckoo search algorithm. A new relevance measure called “Weighted Dominance” has been considered to evaluate the quality of the generated ARs. Extensive experiments have been conducted using different datasets. The results obtained demonstrated the effectiveness of combining DM and optimization algorithms compared to the first approach. Finally, a case study related to an Algerian mobile phone company has been presented illustrating the use of our framework in the decision‐making process.