An Enhanced Generalized Sequential Pattern Classification for Sequence Datasets
Immaculate Mercy A1, Chidambaram M2
1Immaculate Mercy.A*, Ph.D in Computer Science from Bharathidasan University, Tiruchirapalli.
2Chidamabaram .M, Assistant Professor in Computer science in RSGC, Thanjavur.
Manuscript received on October 15, 2019. | Revised Manuscript received on 23 October, 2019. | Manuscript published on November 10, 2019. | PP: 2523-2529 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4895119119/2019©BEIESP | DOI: 10.35940/ijitee.A4895.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A Niche approach for classifying sequence Datasets is achieved by the use of EGSP (Enhanced Generalized sequential pattern) algorithm. The EGSP brings out a prediction model for working with the Sequence Datasets in the domain of Gene and Protein datasets. The Method proceeds by the way of generalizing the datasets of both the supervised and Semisupervised data. The generalization brings out the candidate sequences which paves path for a distinct component extraction. The sequences are generated based on the threshold value which is then followed by applying the EGSP algorithm which brings out the sequential pattern from the pruned sequences. The extracted sequential pattern is then clustered using a gene clustering algorithm. The algorithm (MNBC) Modified Naïve Bayes Classification computes the probabilistic components for each class. The accuracy obtained is far better than the traditional classification algorithms. The resultant classification provides a solution for prediction methods for the selected domain and its applications. The algorithm used gives an upper hand over the computational costs which have been drastically minimized over the existing methods.
Keywords: EGSP, MNBC, Candidate Sequences, Gene Clustering, Generalization,.
Scope of the Article: Clustering