K- Means Clustering on Based Classification Method of Sales Agent
DOI:
https://doi.org/10.35134/jcsitech.v7i2.1Keywords:
Data Mining, k-means, Clustering, Agent, distributorAbstract
Agent is one of very important assets for distributors. A better knowledge of the agents and their behavior is required, particularly to support decisions related to the company's business strategy and to manage a better relationship with distributors. Such knowledge can be obtained by classifying agents based on their behavior through historical data, such as the sale and purchase transaction data. One approach that can be done is a segmentation approach can be done by dividing the agents into several segments. In this paper, Data Mining techniques i.e. K-means clustering method is exploredto classify sales agents. By implementing k-means, the knowledge about the best agents can be acquired along with the agents that have least contribution to the distributor.
References
Morwitz, V.G. and Schmittlein, D., 1992. Using segmentation to improve sales forecasts based on
purchase intent: Which" intenders" actually buy?.Journal of marketing research, pp.391-405.
Bucklin, R.E., Gupta, S. and Siddarth, S., 1998. Determining segmentation in sales response across
consumer purchase behaviors. journal of Marketing Research, pp.189-197.
Jiawei,H, Micheline, K dan Jian, “Data Mining Concepts and Techniques”, 3rd edition P.2011.
Birant, D.,“Data Mining Using RFM Analysis, Knowledge-Oriented Application in Data Mining”,
Herawan, T., Deris, M.M. and Abawajy, J.H., 2010. A rough set approach for selecting clustering
attribute. Knowledge-Based Systems, 23(3), pp.220-231.
Herawan, T. and Deris, M.M., 2011. A soft set approach for association rules mining. KnowledgeBased Systems, 24(1), pp.186-195.
Shah, H., Herawan, T., Ghazali, R., Naseem, R., Aziz, M.A. and Abawajy, J.H., 2014, November. An
Improved Gbest Guided Artificial Bee Colony (IGGABC) Algorithm for Classification and Prediction
Tasks. In International Conference on Neural Information Processing (pp. 559-569). Springer
International Publishing.
Bakar, S.Z.A., Ghazali, R., Ismail, L.H., Herawan, T. and Lasisi, A., 2014. Implementation of
Modified Cuckoo Search Algorithm on Functional Link Neural Network for Climate Change
Prediction via Temperature and Ozone Data. In Recent Advances on Soft Computing and Data Mining
(pp. 239-247). Springer International Publishing.
Mamat, R., Herawan, T. and Deris, M.M., 2013. MAR: Maximum Attribute Relative of soft set for
clustering attribute selection. Knowledge-Based Systems, 52, pp.11-20.
Amini, A., Saboohi, H., Wah, T.Y. and Herawan, T., 2014. DMM-Stream: a density mini-micro
clustering algorithm for evolving data streams. In Proceedings of the First International Conference on
Advanced Data and Information Engineering (DaEng-2013) (pp. 675-682). Springer Singapore.
Qin, H., Ma, X., Zain, J.M. and Herawan, T., 2012. A novel soft set approach in selecting clustering
attribute. Knowledge-Based Systems, 36, pp.139-145.
Mohd, W.M.B.W., Beg, A.H., Herawan, T., Noraziah, A. and Rabbi, K.F., 2011. Improved
Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters. International
Journal of Information Retrieval Research (IJIRR), 1(3), pp.1-14.
Qin, H., Ma, X., Herawan, T. and Zain, J.M., 2012, May. An improved genetic clustering algorithm for
categorical data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 100-111).
Springer Berlin Heidelberg.
Qin, H., Ma, X., Zain, J.M., Sulaiman, N. and Herawan, T., 2011, June. A Mean Mutual Information
Based Approach for Selecting Clustering Attribute. In International Conference on Software
Engineering and Computer Systems (pp. 1-15). Springer Berlin Heidelberg.
Guleria, P danSood, M.,”Data Mining in Education”,The International Journal of Data Mining and
Knowledge Management Process, 4(5):47-60, 2014.
Joshi, A dan Kaur, R.,“Comparative Study of Various Clustering Technique in Data Mining”,The
International Journal of Advanced Research in Computer Science and Software Engineering, 3(3):55-
, 2013.
Mohd, W.M.W., Beg, A.H., Herawan, T. and Rabbi, K.F., 2012. MaxD K-Means: A Clustering
Algorithm for Auto-generation of Centroids and Distance of Data Points in Clusters. In
Computational Intelligence and Intelligent Systems (pp. 192-199). Springer Berlin Heidelberg.
Verma, M et al,“A Comparative Study of Various Clustering Algorithm in Data Mining”,The
International journal of Engineering Research and Application. 2(3):1379-1384, 2012.
Pallavi and Godara, S.,“A Comparative Performance Analysis of Clustering Algorithms”,The
International journal of Engineering Research and Application, 1(3):441-445, 2010.
Aastha,J danRajneet, K.,“Comparative Study of Various Clustering Technique in Data Mining”,The
International Journal of Advanced Research in Computer Science and Software Engineering, 3(3):55-
, 2013.
Arora, A dan Vohra, R.,“Segmentation of Mobile Customer for Improving Profitability Using Data
Mining Technique”,The International Journal of Computer Science and Information Technologies,
(4)5241-5244, 2014.
Golmah, V. and Mirhashemi, G., 2012. Implementing a data mining solution to customer
segmentation for decayable products-a case study for a textile firm. International Journal of Database
Theory and Application, 5(3), pp.73-90.
Ziafat, H danShakeri, M.,“Using Data Mining Technique in Customer Segmentation”,The
International Journal of Engineering Research and Application,4(3): 70-79, 2014.
Silwattananusarn, T danTuamsuk, K.,“Data Mining and Its Application for Knowledge
Management”,The International Journal of Data Mining and Knowledge Management
Process,.2(5):13-24, 2012.
R ajagopal, S.,“Customer Data Clustering Using Data Mining Technique”,The International Journal
of Database Management System, 3(4):1-9, 2011.
Kashwan, R.K.,“Customer Segmentation Using Clustering and Data Mining Technique”,The
International Journal of Computer Theory and Engineering,5(6):856-861, 2013.
Balaji, S. and Srivatsa, S.K., 2012. Customer segmentation for decision support using clustering and
association rule based approaches. International Journal of Computer Science & Engineering
Technology, 3(11), pp.525-529.
Anshul, A danRajan, V.,“Segmentation of Mobile Customer for Improving Profitability Using Data
Mining Technique”,The International Journal of Computer Science and Information Technologies,
(4)5241-5244, 2014.
Vahid, G danGolsa, M.,“Implementing a Data Mining Solution to Customer Segmentation for
Decayable Product”,The International Journal of Database Theory and Application, 5(3):73-89,
Chen, Y.L., Kuo, M.H., Wu, S.Y. and Tang, K., 2009. Discovering recency, frequency, and monetary
(RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and
Applications, 8(5), pp.241-251.
Enny,K, Ujang,S, L,Noor,YdanAsep, S., “Customer Loyality and Profitability”,The international
Journal of Marketing Studies, 5(6):62-72, 2013.
Herawan, T., Rose, A.N.M. and Deris, M.M., 2009. Soft set theoretic approach for dimensionality
reduction. In Database Theory and Application (pp. 171-178). Springer Berlin Heidelberg.
Herawan, T., Ghazali, R. and Deris, M.M., 2010. Soft set theoretic approach for dimensionality
reduction. International Journal of Database Theory and Application, 3(2), pp.4-60.
JCSITech has launched a new template since Vol. 8, No. 2, (2022)
