Mining of colossal patterns is used to mine patterns in databases with many attributes and values, but the number of instances in each database is small. Although many efficient approaches for extracting colossal patterns have been proposed, they cannot be applied to colossal pattern mining with constraints. In this paper, we solve the challenge of extracting colossal patterns with length constraints. Firstly, we describe the problems of min-length constraint and max-length constraint and combine them with length constraints. After that, we evolve a proposal for efficiently truncating candidates in the mining process and another one for fast checking of candidates. Based on these properties, we offer the mining algorithm of Length Constrain...
Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complet...
Abstract. Sequential pattern mining is an important data mining task with wide applications. However...
Finding prevalent patterns in large amount of data has been one of the major problems in the area of...
Mining association rules plays an important role in decision support systems. To mine strong associa...
89 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.We studied the problem of cons...
With the rapid development of information technology and the application of information technology i...
Association rule mining is a popular and well researched data mining functionality to discover inter...
Abstract. When computationally feasible, mining huge databases produces tremendously large numbers o...
Extensive research for frequent-pattern mining in the past decade has brought forth a number of patt...
[[abstract]]Data mining is the process of extracting desirable knowledge or interesting patterns fro...
Abstract. It is known that algorithms for discovering association rules generate an overwhelming num...
AbstractIn this paper, DPMine, a new approach for discovering large Colossal Pattern Sequences from ...
AbstractData mining is the process of extracting desirable knowledge or interesting patterns from ex...
Frequent itemset mining is today one of the most popular data mining techniques. Its application is,...
Multiple longest common subsequence (MLCS) mining (a classical NP-hard problem) is an important tas...
Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complet...
Abstract. Sequential pattern mining is an important data mining task with wide applications. However...
Finding prevalent patterns in large amount of data has been one of the major problems in the area of...
Mining association rules plays an important role in decision support systems. To mine strong associa...
89 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.We studied the problem of cons...
With the rapid development of information technology and the application of information technology i...
Association rule mining is a popular and well researched data mining functionality to discover inter...
Abstract. When computationally feasible, mining huge databases produces tremendously large numbers o...
Extensive research for frequent-pattern mining in the past decade has brought forth a number of patt...
[[abstract]]Data mining is the process of extracting desirable knowledge or interesting patterns fro...
Abstract. It is known that algorithms for discovering association rules generate an overwhelming num...
AbstractIn this paper, DPMine, a new approach for discovering large Colossal Pattern Sequences from ...
AbstractData mining is the process of extracting desirable knowledge or interesting patterns from ex...
Frequent itemset mining is today one of the most popular data mining techniques. Its application is,...
Multiple longest common subsequence (MLCS) mining (a classical NP-hard problem) is an important tas...
Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complet...
Abstract. Sequential pattern mining is an important data mining task with wide applications. However...
Finding prevalent patterns in large amount of data has been one of the major problems in the area of...