IJSER © 2012 http://www.ijser.org Comparative Analysis of k-means and Enhanced K-means clustering algorithm for data mining Neha Aggarwal,Kirti Aggarwal, Kanika gupta ABSTRACT-K-Means Clustering is an immensely popular clustering algorithm for data mining which partitions data into different clusters on the basis of similarity between the data points and aims at maximizing the intra-class simi...

A central part of many algorithms for mining association rules in large data sets is a procedure that finds so called frequent itemsets. This paper proposes a new approach to finding frequent itemsets. The approach reduces a number of passes through an input data set and generalises a number of strategies proposed so far. The idea is to analyse a variable number n of itemset lattice levels in p...

Stochastic nature of earthquake has raised a challenge for engineers to choose which record for their analyses. Clustering is offered as a solution for such a data mining problem to automatically distinguish between ground motion records based on similarities in the corresponding seismic attributes. The present work formulates an optimization problem to seek for the best clustering measures. In...

Educational Data Mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses different computational approaches to analyze educational data in order to study educational questions. Different data mining techniques has been applied in this area. One of the most widely used techniques in EDM...

Finding association rules is an important data mining problem and can be derived based on mining large frequent candidate sets. In this paper, a new algorithm for efficient generating large frequent candidate sets is proposed, which is called Matrix Algorithm. The algorithm generates a matrix which entries 1 or 0 by passing over the cruel database only once, and then the frequent candidate sets...

Providing the right products, at place and time, according to their customer’s preferences, is a problem-seeking solution, especially for companies operating in retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. The proposed approach consists of following steps: (1) customer segmentation; (2) adding location dimension...

-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...

The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and cor...

The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Association rules hiding algorithms get strong and efficient performance for protecting confidential and crucial data. The objective of the proposed Association rule hiding algorithm for privacy preserving data mining is to hi...