Research on Comprehensive Clustering for Smart Phone Configuration

Abstract:

Smartphone has become the necessities of people in the world today. The worlds biggest Smartphone market is china. For Chinese mobile phone manufacturers, if they wanted to share on the global mobile phone market and gain more competitiveness, they would not only in cost and price to win,but also analyze consumer trends and consumer preferences about the smart phone configuration. China mobile is the country's biggest mobile operator and mobile phone distributor company, which has a large amount of smart phone sales data. This paper based on the smart phone sales data, proposed comprehensive clustering method and realized analysis of mobile phone configuration, including DBSCAN algorithm and K-means algorithm. Which cluster and divide the smart phone configuration into the different groups objectively and scientifically. Finally, the simulation analysis of smart phone sales data from china mobile, it is concluded that the accurate clustering results would help enterprises to make better decisions and predictions. Keywords: Smart phone configuration, Comprehensive clustering, DBSCAN algorithm, K-means algorithm

• Periodical: Advanced Materials Research (Volumes 926-930)
• Pages: 2541-2545
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.2541
• Online since: May 2014
• Authors: Juan Yang*
• Keywords: Comprehensive Clustering, DBSCAN Algorithm, K-Means Algorithm, Smart Phone Configuration

References

[1] Dunham M H, Data mining: Introductory and advanced topics, Pearson Education India, (2006).

[2] Hartigan J A, Wong M A, Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 1979, 28(1): 100-108.

DOI: 10.2307/2346830

[3] Kanungo T, Mount D M, Netanyahu N S, et al, An efficient k-means clustering algorithm: Analysis and implementation, Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002, 24(7): 881-892.

DOI: 10.1109/tpami.2002.1017616

[4] Birant D, Kut A. ST-DBSCAN: An algorithm for clustering spatial–temporal data, Data & Knowledge Engineering, 2007, 60(1): 208-221.

DOI: 10.1016/j.datak.2006.01.013

[5] Erman J, Arlitt M, Mahanti A, Traffic classification using clustering algorithms, Proceedings of the 2006 SIGCOMM workshop on Mining network data, ACM, 2006: 281-286.

DOI: 10.1145/1162678.1162679

[6] Ester M, Kriegel H P, Sander J, et al, A density-based algorithm for discovering clusters in large spatial databases with noise, KDD. 1996, 96: 226-231.

[7] Ester M, Kriegel H P, Sander J, et al, Incremental clustering for mining in a data warehousing environment, VLDB. 1998, 98: 323-333.

[8] Toroslu I H, Science in Computor Engineering, Middle East Technical University by, MIDDLE EAST TECHNICAL UNIVERSITY, (2013).

[9] Thangsupachai N, Kitwatthanathawon P, Wanapu S, et al, Clustering Large Datasets with Apriori-based Algorithm and Concurrent Processing, Proceedings of the International MultiConference of Engineers and Computer Scientists, 2011, 1.

[10] Hall M, Frank E, Holmes G, et al, The WEKA data mining software: an update, ACM SIGKDD Explorations Newsletter, 2009, 11(1): 10-18.

DOI: 10.1145/1656274.1656278

[11] Information on http: /www. cs. waikato. ac. nz/ml/weka.