000			02110nam a2200277Ia 4500
003			OSt
005			20260116164551.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9781439857960
020			_aSL01599485
037			_cTextbook
040			_aCSL _beng _cCSL
041			_aeng
084			_aB964 Q5 TB _qCSL
100			_aRitter, Gunter _9898407
245		0	_aRobust cluster analysis and variable selection
260			_aBoca, _bRaton CRC: _c2015.
300			_axx, 371p. _b: ill.
500			_aAppendix A-G 241-338p.; References 339-364p.; Index 365-371p.
520			_aClustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of both applications, describing scenarios in which accuracy and speed are the primary goals. Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. The book illustrates the different methods with simulated data and applies them to real-world data sets that can be easily downloaded from the web. This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals.
650			_a Robustification _9898408
650			_a Topology _9898409
650			_aAlgorithms _9898410
942			_hB964 Q5 TB _cTB _2CC _n0
999			_c6590 _d6590