In the modern world, where data is in abundant, the problem of segregating data that leads to a specific purpose is the order of the hour. Traditionally we use data mining to achieve the purpose. A good example of the above mentioned problem can be found in a store. Generally, we use market basket analysis to find out which groups of products have a high chance of selling together. In this pape...

In this paper we propose Weighed Quantum behaved Particle Swarm Optimization (WQPSO) algorithm for improving the performance of association rule mining algorithm Apriori. It is a global convergence guaranteed algorithm, which outperforms original PSO algorithm and it has fewer parameters to control the search ability of PSO. Finding minimum support and minimum confidence values for mining assoc...

Data mining is a field which searches for interesting knowledge or information from existing massive collection of data. In particular, algorithms like Apriori help a researcher to understand the potential knowledge, deep inside the data base. But due to the large time consumed by Apriori to find the frequent item sets and generate rules, several applications cannot use this algorithm. In this ...

-Generating association rules is generally done by association rule mining algorithm like classical algorithm Apriori, FP-Tree Partition and so on. Genetic algorithm can also implement to generate association rules. Main advantage of Genetic algorithm is that it can perform global search. This paper presents proposed algorithm Multi-Fitness function Genetic algorithm (MFGA) based association ru...

Document Clustering is one of the main themes in text mining. It refers to the process of grouping documents with similar contents or topics into clusters to improve both availability and reliability of text mining applications. Some of the recent algorithms address the problem of high dimensionality of the text by using frequent termsets for clustering. Although the drawbacks of the Apriori al...

Discovering frequent objects (item sets, sequential patterns) is one of the most vital fields in data mining. It is well understood that it require running time and memory for defining candidates and this is the motivation for developing large number of algorithm. Frequent patterns mining is the paying attention research issue in association rules analysis. Apriori algorithm is a standard algor...

Data clustering is a process of putting similar data into groups. A clustering algorithms partition data set into several groups such that the similarity within a group is larger than among groups. Association rule is one of the possible methods for analysis of data. The association rules algorithm generates a huge number of association rules, of which many are redundant. The main idea of this ...

Knowledge exploration from the large set of data, generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how t...

In the field of data mining, classification and association set rules are two of very important techniques to find out new patterns. K-nearest neighbor and apriori algorithm are most usable methods of classification and association set rules respectively. However, individually they face few challenges, such as, time utilization and inefficiency for very large databases. The current paper attemp...