Dmitri A. Viattchenin's A Heuristic Approach to Possibilistic Clustering: Algorithms PDF

By Dmitri A. Viattchenin

ISBN-10: 3642355358

ISBN-13: 9783642355356

ISBN-10: 3642355366

ISBN-13: 9783642355363

The current publication outlines a brand new method of possibilistic clustering within which the sought clustering constitution of the set of items is predicated at once at the formal definition of fuzzy cluster and the possibilistic memberships are made up our minds at once from the values of the pairwise similarity of items. The proposed process can be utilized for fixing varied type difficulties. right here, a few strategies that may be precious at this goal are defined, together with a technique for developing a collection of categorized items for a semi-supervised clustering set of rules, a strategy for decreasing analyzed characteristic area dimensionality and a equipment for uneven information processing. in addition, a method for developing a subset of the main acceptable possible choices for a suite of susceptible fuzzy choice kinfolk, that are outlined on a universe of choices, is defined intimately, and a mode for speedily prototyping the Mamdani’s fuzzy inference structures is brought. This ebook addresses engineers, scientists, professors, scholars and post-graduate scholars, who're attracted to and paintings with fuzzy clustering and its applications

Show description

Read Online or Download A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications PDF

Similar data mining books

Read e-book online Lecture Notes in Artificial Intelligence, Volume 4571, PDF

MLDM / ICDM Medaillie Meissner Porcellan, the “White Gold” of King August the most powerful of Saxonia Gottfried Wilhelm von Leibniz, the nice mathematician and son of Leipzig, used to be observing over us in the course of our occasion in computing device studying and information Mining in trend acceptance (MLDM 2007). He might be pleased with what we've accomplished during this zone to date.

Download e-book for kindle: Machine Learning and Data Mining in Pattern Recognition: 4th by Petra Perner, Atsushi Imiya

This ebook constitutes the refereed court cases of the 4th overseas convention on desktop studying and information Mining in development reputation, MLDM 2005, held in Leipzig, Germany, in July 2005. The sixty eight revised complete papers awarded have been conscientiously reviewed and chosen. The papers are prepared in topical sections on category and version estimation, neural tools, subspace tools, fundamentals and purposes of clustering, characteristic grouping, discretization, choice and transformation, functions in drugs, time sequence and sequential trend mining, mining photos in machine imaginative and prescient, mining pictures and texture, mining movement from series, speech research, elements of information mining, textual content mining, and as a unique music: business purposes of information mining.

Download e-book for iPad: Patterns of Data Modeling (Emerging Directions in Database by Michael Blaha

Best-selling writer and database professional with greater than 25 years of expertise modeling software and firm info, Dr. Michael Blaha offers attempted and validated facts version styles, to assist readers keep away from universal modeling error and pointless frustration on their option to construction potent information types.

Download e-book for kindle: Multiple Classifier Systems: 12th International Workshop, by Friedhelm Schwenker, Fabio Roli, Josef Kittler

This e-book constitutes the refereed court cases of the twelfth foreign Workshop on a number of Classifier structures, MCS 2015, held in Günzburg, Germany, in June/July 2015. the nineteen revised papers offered have been rigorously reviewed and chosen from 25 submissions. The papers handle concerns in a number of classifier platforms and ensemble equipment, together with development reputation, computer studying, neural community, information mining and information.

Additional info for A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications

Sample text

Let X = {x1 ,  , x n } be the finite universe and R be a fuzzy relation on X with μ R ( x i , x j ) being its membership function. 54) for all α  ∈ (0, Proj( R)] . Proof. 23) can be rewritten as Proj( R) = max μ R ( xi , x j ) , xi , x j ∀xi , x j ∈ X . If the fuzzy relation R is the subnormal fuzzy relation, then Proj( R ) < 1 . So, the values of the membership function μ R ( x i , x j ) are absent on the interval (Proj( R ),1] and α  ∉ (Proj( R),1] . On the other hand, if the fuzzy R is the α  ∈ (0, Proj( R) = 1] .

Proofs of these corollaries are obvious and are omitted here. 1. 102), then the supports { Aα1 ,, Aαc } of the α -cores { A(1α ) ,, A(cα ) } of the fuzzy clusters form a partition of a set of objects into disjoint sets. 2. 104), then the crisp coverage which is formed by the supports { Aα1 ,, Aαc } of the α -cores { A(1α ) ,, A(cα ) } of fuzzy clusters of the fuzzy c partition P ( X ) = { A1 ,, Ac } is minimal. 38 1 Introductioon So, the notion of the α -cores of fuzzy clusters allows to include each object oof X = {x1 ,, xn } in the smallest s number ~ c , 1 ≤ c~ ≤ c of fuzzy clusters of thhe fuzzy c -partition P (X ) = { A1 ,, A c } , which is the result of classification.

Hence, the condition c xi ∈  Aαl , ∀i ∈ {1,, n} is met. 17). Thus, the condition μ A(α~ ) ( xi ) = 0 will be met for some value α~ > αˆ . Hence, the condition c xi ∉ Supp( A(α~ ) ) will be met and thecondition xi ∉  Aαl~ will also be satisfied. l =1 That is why the theorem is correct. □ Some propositions are corollaries of this theorem and these corollaries were formulated also in [128]. Let us consider two most important corollaries. Proofs of these corollaries are obvious and are omitted here.

Download PDF sample

A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications by Dmitri A. Viattchenin


by Paul
4.5

Rated 4.30 of 5 – based on 37 votes