Clustering High--Dimensional Data: First International by Francesco Masulli, Alfredo Petrosino, Stefano Rovetta

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.

Show description

Read or Download Clustering High--Dimensional Data: First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers PDF

Similar data mining books

Mining of Massive Datasets

The recognition of the internet and web trade offers many tremendous huge datasets from which info will be gleaned through facts mining. This ebook specializes in functional algorithms which have been used to resolve key difficulties in info mining and that are used on even the biggest datasets. It starts off with a dialogue of the map-reduce framework, a major device for parallelizing algorithms immediately.

Twitter Data Analytics (SpringerBriefs in Computer Science)

This short presents tools for harnessing Twitter facts to find options to complicated inquiries. The short introduces the method of accumulating information via Twitter’s APIs and provides suggestions for curating huge datasets. The textual content provides examples of Twitter facts with real-world examples, the current demanding situations and complexities of establishing visible analytic instruments, and the simplest recommendations to deal with those matters.

Advances in Natural Language Processing: 9th International Conference on NLP, PolTAL 2014, Warsaw, Poland, September 17-19, 2014. Proceedings

This publication constitutes the refereed lawsuits of the ninth overseas convention on Advances in typical Language Processing, PolTAL 2014, Warsaw, Poland, in September 2014. The 27 revised complete papers and 20 revised brief papers offered have been rigorously reviewed and chosen from eighty three submissions. The papers are prepared in topical sections on morphology, named entity acceptance, time period extraction; lexical semantics; sentence point syntax, semantics, and desktop translation; discourse, coreference answer, automated summarization, and query answering; textual content type, details extraction and data retrieval; and speech processing, language modelling, and spell- and grammar-checking.

Analysis of Large and Complex Data

This e-book bargains a picture of the state of the art in class on the interface among records, desktop technology and alertness fields. The contributions span a large spectrum, from theoretical advancements to functional functions; all of them proportion a powerful computational part. the themes addressed are from the next fields: statistics and information research; computer studying and data Discovery; information research in advertising; info research in Finance and Economics; info research in drugs and the lifestyles Sciences; information research within the Social, Behavioural, and future health Care Sciences; information research in Interdisciplinary domain names; class and topic Indexing in Library and data technology.

Additional resources for Clustering High--Dimensional Data: First International Workshop, CHDD 2012, Naples, Italy, May 15, 2012, Revised Selected Papers

Example text

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.

Download PDF sample

Rated 4.81 of 5 – based on 28 votes