Parallel clustering for data mining in CRM

Abstract

In modern business conditions that are characterized by a stronger process of globalization, uncertainty, risk and competition, companies have to struggle every day to maintain market share and achieving better business results. In order to achieve this, the company must always be a step ahead of the competition. This means anybody must anticipate the needs of its clients and each client must access individual. This work is based on addressing this goal. Due to the fact that it is a large amount of data, it is simply impossible to do manual data analysis. Analyses are left to specially developed programs; a new kind of technology whose goal is precisely the solution of the problems that has been faced in Business Intelligence. Business Intelligence (BI) refers to be a broad set of applications and technologies for data collection, access to data and expert analysis of data, and in order to provide adequate support to the decision making process. BI represents a family of products that includes Data mining Algorithms, Data mining products for creating reports. Improving efficiency in this process is discussed in this work. The M-Clustering algorithm which is conceived in this work provides solution to data mining using clusters in twofolds—setting boundary limits during filtering and historical data processing. Define a set of data to be used for training which can be taken from filtering various attributes and the fields from the classifications set given. The data processing activity will be done using this training datasets to get expected result. This is evaluated for processing actual dataset or further execution for provisional trained dataset preparation. This work covers high-level view of the proposed system along with the processing steps used in the system. It also covers experimental evaluation carried out with customized algorithm implementation in WEKA tool and compared the processing efficiency of experimental data with k-means evaluation.