Widiarina .


ne"> Abstract— The disadvantage of the K-means algorithm is sensitive
to have problems determining the initial partition number of
clusters (k) determining the initial value that is different may
produce different cluster groups. To solve the problem of the
sensitivity of the initial partition number of clusters in K-means
algorithm, the proposed algorithm dynamic cluster. The result
showed that the Dynamic K-means algorithm, can produce quality
cluster that is more optimal than the K-means.
IntisariSalah satu kekurangan algoritma K-means yaitu
mempunyai masalah sensitif terhadap penentuan partisi awal
jumlah cluster(k) penentuan nilai awal yang berbeda mungkin
dapat menghasilkan kelompok cluster yang berbeda pula. Untuk
menyelesaikan masalah sensitifitas partisi awal jumlah cluster
pada algoritma K-means, maka diusulkan algoritma cluster
dinamik. Hasil percobaan menunjukan bahwa algoritma
Dynamic K-means, dapat menghasilkan kualitas cluster yang
lebih optimal dibandingkan dengan K-means.
Kata kunci : Segmentasi Pelanggan, K-Means, quality cluster

Full Text:



Adrian, R. Prepaid Telecom Customers Segmentation Using

The K-Mean, The Annals of The University of Oradea

Economic Sciences, 1112–1118. 2012

Aggarwal, N., & Aggarwal, K. Comparative Analysis of kmeans and Enhanced K-means clustering algorithm for data

mining, International Journal of Scientific & Eninnering

Research, 3(3). 2012.

Chen, Y., Zhang, G., Hu, D., & Fu, C. Customer segmentation

based on survival character. Journal of Intelligent

Manufacturing, IEEE 18(4), 513–517. 2007

Deelers, S., & Auwatanamongkol, S. Enhancing K-Means

Algorithm with Initial Cluster Centers Derived from Data

Partitioning along the Data Axis with the Highest Variance,

World Academy of Science, Engineering and Technology,

(December), 323–328. 2007.

Lin, B., & Jones, C. Customer Segmentation Using K-Means

Clustering and Decision Tree: A Research Review, SouthWest

Decision Sciences. 2010

Maulik, U., & Bandyopadhyay, S. Performance Evaluation of

Some Clustering Algorithms and Validity Indices, IEEE

Transaction On Pattern Analysis And Machine Intelligence,

(12), 1650–1654. 2002.

Myatt, G. J. Making Sense of Data: A Practical Guide to

Exploratory Data Analysis and Data Mining. Hoboken: John

Willey & Sons. 2007.

Varcellis, Carlo. Business Intelligence: Data Mining and

Optimization for Decision Making. Southrn Gate, Chichester,

West Sussex: John Willey & Sons, Ltd. 2009.

Witten, I. H., Frank, E., & Hall, M. A. Data Mining: Practical

Machine Learning and Tool. Burlington: Morgan Kaufmann


Wu, Xindong & Kumar, Vipin. The Top Ten Algorithms in

Data Mining. London: CRC Press. 2009.

Yi, B., Qiao, H., Yang, F., & Xu, C. An Improved Initialization

Center Algorithm for K-Means Clustering. International

Conference on Computational Intelligence and Software

Engineering, IEEE (1), 1–4. 2010.

Zhang, C., & Fang, Z. An Improved K-means Clustering

Algorithm Traditional K-mean Algorithm, Journal of

Information & Computational Science, 1, 193–199. 2013.

DOI: https://doi.org/10.31294/jtk.v1i2.259


 dipublikasikan oleh LPPM Universitas Bina Sarana Informatika Jakarta

Jl. Dewi Sartika No. 289, Cawang, Jakarta Timur Telp : 021-8010836
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License