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Sequential pattern mining is an important research task in many domains, such as biological science. In this paper, we study the problem of mining frequent patterns from sequences with wildcards. The user can specify the gap constraints with flexibility. Given a subject sequence, a minimal support threshold and a gap constraint, we aim to find frequent patterns whose supports in the sequence are no...
Recently, pattern matching with flexible gap constraints has attracted extensive attention especially in biological sequence analysis and mining patterns from sequences. An issue is to search Maximal Pattern Matching with Gaps and the One-Off Condition (MPMGOOC). Firstly, we introduce the concept of MPMGOOC. In order to solve the problem, we propose some special concepts of Nettree which is different...
In the field of data mining, clustering is one of the important methods. K-Means is a typical distance-based clustering algorithm; 2-tier clustering should implement scalable clustering by means of dividing, sampling and knowledge integrating. Among those tools of distributed processing, Map-Reduce has been widely embraced by both academia and industry. Hadoop is an open-source parallel and distributed...
Learning from noisy data is a challenging and reality issue for real-world data mining applications. Common practices include data cleansing, error detection and classifier ensembling. The essential goal is to reduce noise impacts and enhance the learners built from the noise corrupted data, so as to benefit further data mining procedures. In this paper, we present a novel framework that unifies error...
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