Sequential pattern mining has been an emerging problem in data mining. In this paper, we propose a new algorithm for mining frequent sequences. It processes only one scan of the da...
Database integration of data mining has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specia...
The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data minin...
Shengnan Cong, Jiawei Han, Jay Hoeflinger, David A...
Sequential pattern mining is very important because it is the basis of many applications. Yet how to efficiently implement the mining is difficult due to the inherent characteri...
During the last decade, sequential pattern mining has been the core of numerous researches. It is now possible to efficiently discover users’ behavior in various domains such a...
The shear volume of the results in traditional support based frequent sequential pattern mining methods has led to increasing interest in new intelligent mining methods to find mo...
Sequential pattern mining is an active field in the domain of knowledge discovery. Recently, with the constant progress in hardware technologies, real-world databases tend to gro...
Sequential pattern mining is very important because it is the basis of many applications. Although there has been a great deal of effort on sequential pattern mining in recent year...
Sequential pattern mining is an interesting data mining problem with many real-world applications. This problem has been studied extensively in static databases. However, in recen...
This paper proposes the integration of semantic information drawn from a web application’s domain knowledge into all phases of the web usage mining process (preprocessing, patte...