Optimization Of Association Rules Research Articles

Optimization of association rules using hybrid data mining technique

Software quality has been the important area of interest for decades now in the IT sector and software firms. Defect prediction gives the tester the pointers as to where the bugs will most likely be hidden in the software product. Identifying and reporting the defect probe areas is the main job of software defect prediction techniques. Early detection of software defects during Software Development Life Cycle could lead to a reduction in cost of development, time involved in further testing activities and rework effort post-production and maintenance phase, thus resulting in more reliable software. Software metrics can be used for developing the defect prediction models. Several data mining techniques can be applied on the available open-source software datasets. These datasets are extracted from software programs. Such datasets made publicly available by National Aeronautics and Space Administration for their various softwares have been extensively used in software engineering-related research activities. These datasets contain information on associated Software Metrics at module level. The proposed idea is a novel hybrid data mining technique consisting of Clustering and Modified Apriori Algorithm that results in improved efficiency and reliability of Software Defect Prediction. This technique works by reducing the number of association rules generated. The results are achieved by using interestingness measure called spread. The paper also does a comparative analysis of the results obtained from the novel technique with the existing hybrid technique of Clustering and Apriori.

Research on Optimization of Association Rules Mining Algorithm

The association rules mining algorithm is always one of the specialists most concerned topics in the research of database knowledge. It is widely used in many fields in society. This article is based on association rule mining algorithm to put forward the relevant strategies for the majorization to make the mining algorithm get better application. With the development of the society, social information becomes richer and richer, people also become more and more interested in the unknown world. Sustainably improve the automation and intelligentization of the tools for processing information can continually improve the abilities in many ways and get more useful information from the huge database. So the appearance of the data mining makes it possible. It can fetch some hidden, unknown and useful information and knowledge from the huge practical application data. And the association rule is a main mode in research of data mining. It can fetch target data by finding the dependency between fields of a given condition from different data fields. As a result, the optimization of association rules mining algorithm is of quite importance in fetching target data.

Optimization of Association Rules for Students' Data: A Soft Set

Association rule (AR) mining is a common method to find the associations between objects in a transaction or in a data set, which is simple logical rules, determine the critical relationship among the objects. The rule generation is difficult if it satisfies a predefined threshold value. To trace the relationship is very important for domains to reveal crucial and hidden information from the data set. Some critical relationship of the objects may appear rarely and to trace them using classical methods are difficult one. The apriori algorithm (AA) with hashing technique along with the PDI algorithm is used to find the appropriate ARs from the students’ dataset. Since soft set is a tool for handling imprecise parameterized data, the soft set concept is used in optimization process to get optimized ARs. In this paper, the ARs are optimized using soft set for further analysis.

Comparison of Heuristics for Optimization of Association Rules

In this paper, five greedy heuristics for construction of association rules are compared from the point of view of the length and coverage of constructed rules. The obtained rules are compared also with optimal ones constructed by dynamic programming algorithms. The average relative difference between length of rules constructed by the best heuristic and minimum length of rules is at most 4%. The same situation is with coverage.

Dynamic Programming Approach for Construction of Association Rule Systems

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work.

Improved Association Rules Optimization using Modified ABC Algorithm

To discover the frequent item sets from the huge data sets, one of the most popular techniques of data mining, called association rule mining technique used. For generating association rules from huge database using association rule mining technique, Computer system takes too much. This can be enhanced, if the number of association rules generated using association rule mining technique from a huge dataset can be optimized. So here in this work, firstly association rules are generated using standard Apriori algorithm and then optimized these association rules using modified artificial bee colony (ABC) algorithm. In this modified ABC algorithm, one additional operator, called crossover operator, is used after the third phase, called scout bee phase, of ABC algorithm. Due to the better exploration property of crossover operator, it is used in this work. Experimental results show that the proposed schemes performance better than previously proposed schemes like K-Nearest Neighbor algorithm (KNN) and ABC algorithm. Keywordsbee colony algorithm, ABC, Crossover operator, Association rules, Support, Confidence, Frequent item sets

Association Rules Optimization using Artificial Bee Colony Algorithm with Mutation

data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using artificial bee colony algorithm (ABC). The Artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, artificial bee colony algorithm with mutation operator is used to generate high quality association rules for finding frequent item sets from large data sets. The mutation operator is used after the scout bee phase in this work. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contains negative attributes. Keywordsbee colony (ABC), Mutation, Association rule, Support, Confidence, Frequent item set, Data mining.

Association Rules Optimization Algorithm Based on Fuzzy Clustering

Frequent pattern mining has been an important research direction in association rules. This paper use a methodology by preprocessing the original dataset using fuzzy clustering which can mapped quantitative datasets into linguistic datasets. Then we propose a algorithm based on fuzzy frequent pattern tree for extracting fuzzy frequent itemset from mapped linguistic datasets. Experimental results show that our algorithm is shorter than the F-Apriori on computing time to huge database. For large database, the algorithm presented in this paper is proved to have a good prospect.