IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 3    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 3,March 2012
Single Link clustering on data sets[
Full Text(PDF, )  PP.584-588  
Ajaya Kushwaha, Manojeet Roy
clustering, nearest neighbor, reciprocal nearest neighbor, complete link, probabilistic analysis.
Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with low distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Most data-mining methods assume data is in the form of a feature-vector (asingle relational table) and cannot handle multi-relational data. Inductive logic programming is a form of relational data mining that discovers rules in _first-order logic from multi-relational data. This paper discusses the application of SLINK to learning patterns for link discovery. Clustering is among the oldest techniques used in data mining applications. Typical implementations of the hierarchical agglomerative clustering methods (HACM) require an amount of O(N2)-space when there are N data objects, making such algorithms impractical for problems involving large datasets. The well-known clustering algorithm RNN-CLINK requires only O(N)-space but O(N3)-time in the worst case, although the average time appears to be O(N2 log N).
[1] Li Zhan, Liu Zhijing, , ‗ Web Mining Based On Multi-Agents ‘, COMPUTER SOCIETY,IEEE(2003)

[2] Margaret H. Dunham and Sridhar, Data Mining, Introduction and Advanced Topics, (Prenticce Hall Publication), ISBN81-7758-785-4, chap nos.1,7, pp.3,4,195-218.

[3] M. R. Anderberg, Cluster Analysis for Applications, Academic Press, New York, 1973.

[4] A. Berson and S. J. Smith, Data Warehousing, Data Mining, and OLAP, McGraw-Hill, New York, 1997.

[5] Jang, J.-S. R., Sun, C.-T., Mizutani, E., ―Neuro- Fuzzy and Soft Computing –A Computational Approach to Learning and Machine Intelligence,‖ Prentice Hall.

[6] Nauck, D., Kruse, R., Klawonn, F., ―Foundations of Neuro-Fuzzy Systems,‖ John Wiley & Sons Ltd., NY, 1997.

[7] M. S. Chen, J. Han, and P. S. Yu. Data mining: an overview from database perspective. IEEE Trans. On Knowledge and Data Engineering, 5(1):866—883, Dec.1996

[8] W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structures and Algorithms, Prentice Hall, 1992.

[9] Y. Zhao and G. Karypis. Evaluation of hierarchical clusteringalgorithms for document datasets. In CIKM, 2002.

[10] A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.

[11] Lin, C., Lee, C., ―Neural Fuzzy Systems,‖ Prentice Hall, NJ, 1996

[12] Tsoukalas, L., Uhrig, R., ―Fuzzy and NeuralApproaches in Engineering,‖ John Wiley & Sons,Inc., NY, 1997

[13] U.M. Fayyad and P. Smyth. Advances in KnowledgeDiscovery and Data Mining. AAAI/MIT Press, Menlo Park,CA, 1996.

[14] Kaur H, Wasan S K, Al-Hegami A S and Bhatnagar V, A Unified Approach for Discovery of Interesting Association Rules in Medical Databases, Advances in Data Mining, Lecture Notes inArtificial Intelligence, Vol. 4065, Springer- Verlag, Berlin, Heidelberg (2006).

[15] Kaur H and Wasan S K, An Integrated Approach in Medical Decision Making for Eliciting Knowledge, Web-based Applications in Health Care & Biomedicine, Annals of Information Systems (AoIS), ed. A. Lazakidou, Springer

Untitled Page