By Francesco Masulli, Alfredo Petrosino, Stefano Rovetta
This publication constitutes the court cases of the overseas Workshop on Clustering High-Dimensional info, CHDD 2012, held in Naples, Italy, in may possibly 2012.
The nine papers offered during this quantity have been rigorously reviewed and chosen from 15 submissions. They care for the final topic and problems with high-dimensional facts clustering; current examples of strategies used to discover and examine clusters in excessive dimensionality; and the commonest method of take on dimensionality difficulties, specifically, dimensionality aid and its program in clustering.
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Additional resources for Clustering High--Dimensional Data: First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers
Stat. 2, 225–250 (1993) 29. : Variable selection for model-based high-dimensional clustering and its application to microarray data. Biometrics 64, 440–448 (2008) 30. : Fuzzy c-means in high dimensional spaces. Fuzzy Syst. Appl. 1, 1–17 (2011) 31. : A contribution to convergence theory of fuzzy c-means and its derivatives. IEEE Trans. Fuzzy Syst. 11, 682–694 (2003) 32. : What is fuzzy about fuzzy clustering? understanding and improving the concept of the fuzziﬁer. , Borgelt, C. ) Advances in Intelligent Data Analysis, vol.
But missing values will occur with larger probability in high-dimensional data. This is still an open problem. 32 F. Klawonn et al. References 1. : Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961) 2. : When is nearest neighbor meaningful? , Bruneman, P. ) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998) 3. : When is ‘nearest neighbour’ meaningful: a converse theorem and implications. J. Complex. 25(4), 385–397 (2009) 4. : The concentration of fractional distances.
In contrast to this example where the clusters are well separated, the ﬁrst example in Sect. 4 would also cause a problem for the objective function, since the minimum would not be pronounced very clearly. What are Clusters in High Dimensions and are they Diﬃcult to Find? 31 There are ways to partly avoid these problems. One way is to try to adjust the fuzziﬁer w in the objective function (4) depending on the number of dimensions. The higher the number of dimensions, the smaller, but of course larger than 1, the fuzziﬁer should be chosen.