A hybrid algorithm of finding features for clustering sequential data

Proteins are the structural components of living cells and tissues, and thus an important building block in all living organisms. Patterns in proteins sequences are some subsequences which appear frequently. Patterns often denote important functional regions in proteins and can be used to characterize a protein family or discover the function of proteins. Moreover, it provides valuable information about the evolution of species. Grouping protein sequences that share similar structure helps in identifying sequences with similar functionality. Many algorithms have been proposed for clustering proteins according to their similarity. Feature-based clustering algorithms use a near-linear complexity K-means based clustering algorithm. Although feature-based clustering algorithms are scalable and lead to reasonably good clusters, they consume time on performing the global approach and the local approach separately. Therefore, in this paper, we propose the hybrid algorithm to find and mark features for feature-based clustering algorithms. We observe an interesting result from the relation between the local features and the closed frequent sequential patterns. In our hybrid algorithm (CLoseLG), we first find the closed frequent sequential patterns directly. Next, we find local candidate features efficiently from the closed frequent sequential patterns and then mark the local features. Finally, we find and mark the global features. From our performance study based on the biological data and the synthetic data, we show that our proposed hybrid algorithm is more efficient than the feature-based algorithm.