Frequent Itemsets Mining(FIM) is a typical data mining task and has gained much attention. Due to the consideration of individual privacy, various studies have been focusing on privacy-preserving FIM problems. Differential privacy has emerged as a promising scheme for protecting individual privacy in data mining against adversaries with arbitrary background knowledge. In this paper, we present ...

Most of the algorithms for discovering association rules require multiple passes over the database resulting in a large number of disk reads and placing a huge burden on the I/O subsystem [1]. To reduce this bottleneck in case of large databases, a new association rule mining algorithm, which uses both the Partition and the Apriori approach for calculating the frequent item sets in a single pas...

This paper proposes a framework of multisource geo-knowledge discovery with association rules. Taking into account spatial data exist semantic fuzziness, and the conversion between qualitative concept and quantitative description is uncertain, in our study, conceptual partition algorithm and membership grade judgment algorithm based on cloud model was used. Meanwhile, there are many correlation...

Data mining is wide spreading its applications in several areas. There are different tasks in mining which provides solutions for wide variety of problems in order to discover knowledge. Among those tasks association mining plays a pivotal role for identifying frequent patterns. Among the available association mining algorithms Apriori algorithm is one of the most prevalent and dominant algorit...

The classical algorithm for mining association rules is low efficiency. Generally there is high redundancy between gained rules. To solve these problems, a new algorithm of finding non-redundant association rules based on frequent concept sets was proposed. The Hasse graph of these concepts was generated on the basis of the FP-tree. Because of the restriction of the support most Hasse graphs ha...

This paper proposes a new weighted mining frequent pattern based on customer’s RFM(Recency, Frequency, Monetary) score for personalized u-commerce recommendation system under ubiquitous computing. An existing recommendation system using traditional mining has the problem, such as delay of processing speed from a cause of frequent scanning a large data, considering equal weight value of every it...

Abstract— Data mining is a field which explores for exciting knowledge or information from existing substantial group of data. In particular, algorithms like Apriori aid a researcher to understand the potential knowledge, deep inside the database. However because of the huge time consumed by Apriori to find the frequent item sets and generate rules, several applications cannot use this algorith...

The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighed Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weig...

A great research work has been done in last decade in association rules mining (ARM) algorithms . Therefore, various algorithms were proposed to discover frequent item sets and then mine association rules. Apriori algorithm is the most frequently used algorithm for generating association rules. Apriori algorithm has some abuses, such as too many scans of the database, large load of system’s I/O...