By Minos Garofalakis, Rajeev Rastogi (auth.), Ming-Syan Chen, Philip S. Yu, Bing Liu (eds.)
Knowledge discovery and information mining became components of turning out to be importance a result of fresh expanding call for for KDD concepts, together with these utilized in computing device studying, databases, data, wisdom acquisition, facts visualization, and excessive functionality computing. In view of this, and following the good fortune of the 5 past PAKDD meetings, the 6th Pacific-Asia convention on wisdom Discovery and information Mining (PAKDD 2002) aimed to supply a discussion board for the sharing of unique examine effects, leading edge principles, cutting-edge advancements, and implementation reviews in wisdom discovery and knowledge mining between researchers in educational and commercial firms. a lot paintings went into getting ready a software of top of the range. We obtained 128 submissions. each paper was once reviewed via three application committee participants, and 32 have been chosen as normal papers and 20 have been chosen as brief papers, representing a 25% reputation price for normal papers. The PAKDD 2002 software used to be additional improved by way of keynote speeches, brought via Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. furthermore, PAKDD 2002 used to be complemented through 3 tutorials, XML and knowledge mining (by Kyuseok Shim and Surajit Chadhuri), mining buyer info throughout quite a few client touchpoints at- trade websites (by Jaideep Srivastava), and knowledge clustering research, from easy groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).
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Extra resources for Advances in Knowledge Discovery and Data Mining: 6th Pacific-Asia Conference, PAKDD 2002 Taipei, Taiwan, May 6–8, 2002 Proceedings
2002) TURN* unsupervised clustering of spatial data. submitted to ACM-SIKDD Intl. Conf. on Knowledge Discovery and Data Mining, July 2002. 7. Gath I. and Geva A. (1989) Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7). 8. , Rastogi R. and Shim K. (1999) ROCK: a robust clustering algorithm for categorical attributes. In 15th ICDE Int’l Conf. on Data Engineering. 9. , Vazirgiannis M. and Batistakis I. (2000) Quality scheme assessment in the clustering process.
S. S. Yu, and B. ): PAKDD 2002, LNAI 2336, pp. 28–39, 2002. c Springer-Verlag Berlin Heidelberg 2002 On Data Clustering Analysis: Scalability, Constraints, and Validation 29 – Insensitive to the data input order: The clustering method should give consistent results irrespective of the order the data is presented. – Scaleable to high dimensionality: The ability to handle high dimensionality is very challenging but real data sets are often multidimensional. Historically, there is no single algorithm that can fully satisfy all the above requirements.
In the External approach, the clustering result C can be compared to an independent partition of the data P built according to our intuition of the structure of the data set or the proximity matrix P is compared to P. The Internal Criteria approach uses some quantities or features inherent in the data set to evaluate the result. If the clustering is hierarchical, a matrix Pc , representing the proximity level at which two vectors are found in the same cluster for the ﬁrst time, can be compared to P.