Mining hybrid dimension fuzzy association rule is one of the important processes in data mining . Apriori algorithm concerned with handling single level, single dimensional association rules. this paper is presenting, a new modification in joining process to reduce the redundant generation of sub items during pruning the candidate itemsets, which can obtain higher efficiency of mining that of o...

Efficient partitioning of large data sets into homogenous clusters is a fundamental problem in data mining. The standard hierarchical clustering methods provide no solution for this problem due to their computational inefficiency. The k-means based methods are promising for their efficiency in processing large data sets. However, their use is often limited to numeric data. In this paper we pres...

Association rule mining has been an area of active research in the field of knowledge discovery and numerous algorithms have been developed to this end. Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating the influential factors like value (utility), quantity of items sold (weight) and more, for the mining of assoc...

The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than standard k-nn. Mode...

The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than standard k-nn. Mode...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

Mining frequent items and itemsets is a daunting task in large databases and has attracted research attention in recent years. Generating specific itemset, K –itemset having K items, is an interesting research problem in data mining and knowledge discovery. In this paper, we propose an algorithm for finding K itemset frequent pattern generation in large databases which is named as AMKIS. AMKIS ...

For traditional data mining techniques cannot be directly applied to the semi-structured XML data mining problem, this paper proposes a novel ontology and association rules based XML mining algorithm. The algorithm firstly introduces the domain ontology and hash technology to improve the operation of emerging frequent item sets and generating association rules, then uses a hash table to store t...

In this paper, we use transaction data as the source data of mining, and each transaction data contains a consumer ever buy items. We mine association rules from two aspects. One is to present a Boolean FP-tree algorithm to mine association rules with the Boolean computation according to the FP-tree algorithm and CDAR algorithm. The experiments show that the performances of our algorithm are fa...