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...

Frequent pattern mining plays an essential role in many data analysis tasks including association-, correlation-, and causality analysis and has broad applications. Examples are market basket analysis and web click stream analysis. Although a number of efficient methods for mining frequent patterns where proposed in the past, there exist only a small number of visual exploration tools for disco...

Discovering the influencers in the new social network is very important for the promotion of products and the supervision of public opinion. Most of the previous research was based on the method of mining influential individuals, while the tribe-leaders were neglected. In this paper, a new method of mining tribeleaders is proposed based on the frequent pattern of propagation. First, a method of...

Algorithms for frequent pattern mining, a popular informatics application, have unique requirements that are not met by any of the existing parallel tools. In particular, such applications operate on extremely large data sets and have irregular memory access patterns. For efficient parallelization of such applications, it is necessary to support dynamic load balancing along with scheduling mech...

Frequent pattern mining is a process of extracting frequently occurring itemset patterns from very large data storages. These frequent patterns are used to generate association rules which define the relationship among items. The strength of the relationship can be measured using two different units namely support value and confidence level. Any relationship that satisfies minimum threshold of ...

New Approaches to Weighted Frequent Pattern Mining. (December 2005) Unil Yun, B.S., Hong Ik University; M.S., Korea University Chair of Advisory Committee: Dr. John J. Leggett Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use o...

Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation since the prohibitive nature of the cost associated with extracting knowledge, as well as the lack of suitable declarative query language support. We investigate approaches based on SQL for t...

The objective of this thesis is to study data mining query optimization in a Logicbased Knowledge Discovery Support Environment. i.e, a flexible knowledge discovery system with capabilities to obtain, maintain, represent, and utilize both induced and deduced knowledge. In particular, we focus on frequent pattern queries, since this kind of query is at the basis of many mining tasks, and it seem...

Mining frequent patterns fromWeb logs is an important data mining task. Candidate-generation-and-test and pattern-growth are two representative frequent pattern mining approaches. We have conducted extensive experiments on real world Web log data to analyse the characteristics of Web logs and the behaviours of these two approaches on Web logs. To improve the performance of current algorithms on...

Efficient frequent pattern mining algorithms are decisive for mining association rule. In this paper, we examine the matter of association rule mining for items in a massive database of sales transactions. Finding large patterns from database transactions has been suggested in many algorithms like Apriori, DHP, ECLAT, FP Growth etc. But here we have introduced newer algorithm called Improved Fr...