The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. However, the attackers may also extract some private (sensitive) information of the user and this can make threats against the user location privacy. Therefore, location privacy protection bec...

finding frequent patterns plays a key role in exploring association patterns, correlation, and many other interesting relationships that are applicable in tdb. several association rule mining algorithms such as apriori, fp-growth, and eclat have been proposed in the literature. fp-growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract...

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast an...

Several algorithms for association rule mining, have been implemented including a variation of Apriori, an algorithm using hash functions for finding large 2-itemsets and 3-itemsets and direct search method for finding other large k-itemsets, and another variation of Eclat algorithm using perfect hash functions for 2-itemsets and 3-itemsets and the method of vertical mining for finding other la...

Mining association rules is a fundamental data mining task. However, depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing t...

Association Rule mining is one of the important and most popular data mining techniques. It extracts interesting correlations, frequent patterns and associations among sets of items in the transaction databases or other data repositories. Apriori algorithm is an influential algorithm for mining frequent itemsets for Boolean association rules. Firstly, the concept of association rules is introdu...

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with hierarchy rel...

Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the shortages of the existing K-means algorithm of data-mining for customer classification, this paper advances a new customer classification algorithm through improving the existing K...

In 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 particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for s...