Analyzing Sequential Pattern Mining to Detect Calcium Peaks in Cardiomyocytes Data

Abstract

This study examines sequential pattern mining and its applications in various fields. The previous research was conducted by examining signal data, from which calcium peaks were automatically detected and classified. Before the implementation of sequential pattern mining approach to find out patterns from a dataset of 102 signals, association rule mining, frequent itemsets, Apriori algorithm, and rule generation were explored. Sequential pattern mining, including time constraints, are defined, before examining a knowledge-assisted sequential pattern analysis, from which certain points are considered, such as what is a sequential itemset. The implementation phase consists of calculating what constitutes a candidate itemset. The findings are modified to work with a sequential rule mining algorithm, and the results are discussed afterwards