• Dr. D. Mabuni

Sequence data classification and clustering are important operations in machine learning. A quantitative similarity measure is needed to find the similarity between two given input sequences before applying classification and clustering operations of sequence data records. Many well defined and well-designed functional formulas are already available for sequence data similarity measure computations. In this paper a new functional formula is proposed for sequence data similarity finding. This new sequence data similarity finding measure is based on total number of common subsequences present in the given two input sequences and the satisfaction of longest common subsequence (LCS) property of each subsequence present in the given input data sequences. LCS preserves not only the ordering information of symbols but also correctly maintains subsequence relationship details. Proposed formula is simple to use and compute similarity between input pair of sequence data records. This formula is named as Sequence Data Similarity (SDS).SDS similarity measure computation is completely based on given pair of sequences and all possible common subsequences in both the input pair of sequences.

Sequence classification, sequence data classification, sequence data clustering,sequence similarity finding measure, sequential data pattern mining, sequential pattern matching, data sequence classification, sequential data analysis, machine learning.

"A Novel Sequence Data Similarity Finding Technique for Sequence Data Classification and Clustering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.f521-f528, September-2023, Available :http://www.jetir.org/papers/JETIR2309563.pdf

"A Novel Sequence Data Similarity Finding Technique for Sequence Data Classification and Clustering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppf521-f528, September-2023, Available at : http://www.jetir.org/papers/JETIR2309563.pdf