Data Mining Methods for Improving Business Process Modelling

Abstract

This paper introduces a novel methodology to extract core concepts from raw dataset. This methodology is based on data mining and analysis. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.
Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. At the data mining phase the keywords are extracted by tokenizing, removing stop-lists and generating similarity by classification and clustering. For this methodology we are going to use k-NN and k-means algorithms. We applied our methodology on large data set. Similarity based algorithm was interesting and gave us valuable knowledge about content and used for deep analysis.