A study on sequential pattern mining on chemical information

Data mining (DM) is used for extracting the useful and non-trivial information from the large amount of data to collect in many and diverse fields. Data mining determines explanation through clustering visualization, association and sequential analysis. Chemical compounds are well-defined structures compressed by a graph representation. Chemical bonding is the association of atoms into molecules, ions, crystals and other stable species which frame the common substances in chemical information. However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications. In this work, chemical structured index bonding is used for sequential pattern mining. Our research work helps to evaluate the structural patterns of chemical bonding in chemical information data sets.