Approximate Frequent Pattern Research Articles

An Efficient Mining for Approximate Frequent Items in Protein Sequence Database

The rapid increase of available proteins, DNA and other biological sequences has made the problem of discovering the meaningful patterns from sequences, a major task for Bioinformatics research. Data mining of protein sequence databases poses special challenges, because several protein databases are non-relational whereas most of the data mining and machine learning techniques considers the data input to be a relational database. The existing sequence mining algorithms mainly focus on mining for subsequences. However, a wide range of applications such as biological DNA and protein motif mining needs an effective mining for identifying the approximate frequent patterns. The existing approximate frequent pattern mining algorithms have some delimitations such as lack of knowledge to finding the patterns, poor scalability and complexity to adapt into some other applications. In this paper, a Generalized Approximate Pattern Algorithm (GAPA) is proposed to efficiently mine the approximate frequent patterns in the protein sequence database. Pearson’s coefficient correlation is computed among the protein sequence database items to analyze the approximate frequent patterns. The performance of the proposed GAPA is analyzed and tested with the FASTA protein sequence database. FASTA database files hold the protein translations of Ensembl gene predictions. GAPA is compared with the existing methods such as Approximate Frequent Itemsets (AFI) tree and Approximate Closed Frequent Itemsets (ACFIM) in terms of support, accuracy, memory usage and time consumption. The experimental results shows GAPA is scalable and outperforms than the existing algorithms.

확률 기법에 기반한 근접 빈발 패턴 마이닝 기법의 성능평가

근접 빈발 패턴 마이닝은 향상된 효율성을 위해 정확한 패턴보다 허용되는 범위 안에서 근접 빈발 패턴을 마이닝한다. 데이터베이스의 크기가 증대함에 따라 거대한 데이터베이스를 처리하기 위해서 더 빠른 마이닝 기법이 필요하게 되고 있다. 또한, 노이지나 데이터의 다양성 때문에 패턴을 마이닝 하는 것에 대한 정확한 결과를 찾기가 더 어렵다. 이러한 경우들에 대해, 근접 빈발 패턴 마이닝을 함으로 실행시간, 메모리 사용량, 그리고 확장성의 관점에서 더 효율적인 마이닝을 수행할 수 있다. 이 논문에서는 확률 기법에 근간한 근접 패턴 마이닝 알고리즘에 대한 특성을 살펴보고 척도가 되는 확률 기법에 기반한 근접 패턴 마이닝 알고리즘에 대해 성능 평가를 한다. 최종적으로 성능의 향상을 위해 테스트 결과를 분석한다. Approximate Frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we study the characteristics of an approximate mining algorithm based on probabilistic technique and run performance evaluation of the efficient approximate frequent pattern mining algorithm. Finally, we analyze the test results for more improvement.