Abstract
Short text clustering is an essential pre-process in social network analysis, where k-means is one of the most famous clustering algorithms for its simplicity and efficiency. However, k-means is instable and sensitive to the initial cluster centers, and it can be trapped in some local optimums. Moreover, its parameter of cluster number k is hard to be determined accurately. In this paper, we propose an improved k-means algorithm MAKM (MAFIA-based kmeans) equipped with a new feature extraction method TT (Term Transition) to overcome the shortages. In MAKM, the initial centers and the cluster number k are determined by an improved algorithm of Mining Maximal Frequent Item Sets. In TT, we claim that co-occurrence between two words in short text represents greater correlation and each word has certain probabilities of spreading to others. The Experiment on real datasets shows our approach achieves better results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goil, S.: MAFIA: efficient and scalable subspace clustering for very large (1999)
Elkan, C.: Using the triangle inequality to accelerate k-means. In: ICML, pp. 147–153 (2003)
Huang, Z.: Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery 2, 283–304 (1998)
Hassan-Montero, Y., Herrero-Solana, V.: Improving Tag-Clouds as Visual Information Retrieval Interfaces, Spain, October 25-28 (2006)
Bradley, P.S., Fayyad, U.M.: Refining Initial Points for K-Means Clustering. In: ICML (1998)
Lu, J.F., Tang, J.B., Tang, Z.M., Yang, J.Y.: Hierarchical initialization approach for K-Means clustering. Pattern Recognition Letters (2008)
Aggarwal, A., Deshpande, A., Kannan, R.: Adaptive Sampling for k-Means Clustering. In: Dinur, I., Jansen, K., Naor, J., Rolim, J. (eds.) APPROX 2009. LNCS, vol. 5687, pp. 15–28. Springer, Heidelberg (2009)
Arai, K., Barakbah, A.R.: Hierarchical K-means: an algorithm for centroids initialization for K-means. In: Reports of the Faculty of Science and Engineering, vol. 36(1), Saga University, Japan (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, P., Zhang, Y. (2013). MAKM: A MAFIA-Based k-Means Algorithm for Short Text in Social Networks. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-37450-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37449-4
Online ISBN: 978-3-642-37450-0
eBook Packages: Computer ScienceComputer Science (R0)