A novel approach for efficient mining and discrimination of gene sequencing in protein sequence database

Abstract

Sequential pattern mining is the task of identifying the patterns present in a certain number of data instances. The existing sequence mining algorithms mainly focus on mining for sub sequences. 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 delimitation such as lack of knowledge to finding the patterns, poor scalability and complexity to adapt into some other applications. The algorithm Generalised Approximate Pattern mining Algorithm (GAPA) is proposed to efficiently mine the approximate frequent patterns in the protein sequence database. Pearsonand#8223;s coefficient correlation is computed among the protein sequence database items to analyse the approximate frequent patterns. This work proposes a novel Enhanced Sequence Identification (ESI) approach to effectively find the frequent patterns from the huge dataset. The Hybrid Frequent Pattern Mining (HFPM) algorithm employs the tree-based structure that achieves a significant reduction in the space complexity. Association rules are used for mining the frequent patterns by identifying the relationship between the items and finding the approximate frequent patterns from the databases. The frequent items with dependency are added down to the leaves of the tree. The proposed HFPM-ESI algorithm shows high performance newlinewith less memory consumption and lower run time than the existing algorithms. The proposed algorithm ensures the effective extraction of frequent patterns with newlinethe optimization of resource constraints newline newline