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Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ensembles have been examined in the literature, e.g., random feature selection, weak clusterers, random projections, etc. Typically, one heuristic is picked at a time to construct the ensemble. To increase diversity of the ensemble,...
Summary The chapter deals with use of different soft computing tools to achieve web intelligence. It summarizes different characteristics of web data, the basic components of web mining and its different types, and their current states of the art. The reason for considering web mining, a separate field from data mining, is explained. The limitations of some of the existing web mining methods and tools...
This paper describes Evolvable Hardware (EHW) and its applications to pattern recognition and fault-torelant systems. EHW can change its own hardware structure to adapt to the environment whenever environmental changes (including hardware malfunction) occur. EHW is implemented on a PLD(Programmable Logic Device)-like device whose architecture can be altered by re-programming the architecture bits...
An experimental investigation on selection of a reference set for the k-Nearest Neighbors (k-NN) classification method has been conducted. Genetic algorithms have been employed bringing together the strategy to preserve the decision boundary and that of selecting the most ”typical” objects as prototypes. The chromosome is directly mapped onto the reference set and the best subset is subsequently evolved...
Genetic Algorithms (GAs) are generally portrayed as a search procedure which can optimize functions based on a limited sample of function values. In this paper, an objective function based on minimal spanning tree (MST) of data points is proposed for clustering and GAs have been used in an attempt to optimize the specified objective function in order to detect the natural grouping in a given data...
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