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In this paper we present a sampling approach to run the k-means algorithm in large data sets. We propose a genetic algorithm to guide sampling based on evaluating the fitness of each individual of the population through the k-means clustering algorithm. Although we want a partition with the lowest SSE, our algorithm tries to find the sample with the highest SSE. After finding a good sample the remaining...
This contribution proposes a genetic learning process for designing the knowledge base of Fuzzy Rule-Based classification Systems, that will be used as binary classifiers in a One-vs-One decomposition for multi-class problems. A Genetic Algorithm is designed to adapt the number of fuzzy labels per variable (granularity level) for each classifier in order to improve the accuracy rate of a multi-class...
Clustering classifies data into homogeneous groups such that the objects in each group are similar and the objects between groups are not similar. Cluster algorithms can be available in many literatures. And among them, spectral clustering (SC) is one of the most popular and appealing clustering methods because of its generality, efficiency and its rich theoretical foundation. But SC algorithms need...
This paper proposes the use of a local feature selection scheme, for the effective selection of relevant features, when designing Genetic Fuzzy Rule-Based Classification Systems (GFRBCSs). The method relies in providing the genetic search with deterministic information about the quality of each feature with respect to its classification ability, directing the evolution in selecting the most useful...
This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications. We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters. The algorithm is distance-based and creates centroids. To evaluate the proposed algorithm, we use some artificial data sets and...
Clustering is an important, hard and active topic in data analysis and pattern recognition. K clustering is a branch of data clustering where the number of clusters is know in advance. Recently, spectral clustering (SC) becomes one of the most popular and appealing k clustering methods because of its generality, efficiency and its rich theoretical foundation. But the final results obtained from SCs...
This survey gives state-of-the-art of genetic algorithm (GA) based clustering techniques. Clustering is a fundamental and widely applied method in understanding and exploring a data set. Interest in clustering has increased recently due to the emergence of several new areas of applications including data mining, bioinformatics, web use data analysis, image analysis etc. To enhance the performance...
This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if...then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually affected by their uncertain parameters. The proposed...
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