Efficiently identifying top k similar entities

With the rapid growth in genomic studies, more and more successful researches are being produced that integrate tools and technologies from interdisciplinary sciences. Computational biology or bioinformatics is one such field that successfully applies computational tools to capture and transcribe biological data. Specifically in genomic studies, detection and analysis of co-occurring mutations is an leading area of study. Concurrently, in the recent years, computer science and information technology have seen an increased interest in the area association analysis and co-occurrence computation. The traditional method of finding top similar entities involves examining every possible pair of entities, which leads to a prohibitive quadratic time complexity. Most of the existing approaches also require a similarity measure and threshold beforehand to retrieve the top similar entities. These parameters are not always easy to tune. Heuristically, an adaptive method can have wider applications for identifying the top most similar pair of mutations (or entities in general). In this thesis, we have presented an algorithm to efficiently identify top k similar pair of mutations using co-occurrence as the similarity measure. Our approach used an upperbound condition to iteratively prune the search space and tackled the quadratic complexity. The empirical evaluations show that the proposed approach shows the computational efficiency in terms of execution time and accuracy of our approach particularly in large size datasets. In addition, we also evaluate the impact of various parameters like input size, k on the execution time in top k approaches. This study concludes that systematic pruning of the search space using an adaptive threshold condition optimizes the process of identifying top similar pair of entities