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Fuzzy Clustering is a data clustering technique wherein each data point belongs to a cluster to some degree that is specified by a membership grade.…

Conceptual Clustering is an important and active research area that aims to efficiently cluster and explain the data. It approaches provide descriptions that do not…

Hierarchical Clustering is achieved by use of an appropriate metric, and a linkage criterion which specifies the dissimilarity of sets as a function of the…

Association Rule Learning is generally used to analyze the “market-basket” for retailers. Traditionally, this simply looks at whether a person has purchased an item or…

Apriori Algorithm is an influential algorithm for mining frequent itemsets for boolean association rules. It uses a “bottom up” approach, where frequent subsets are extended…

Self Organizing Map is one of the most popular neural network models. It belongs to the category of competitive learning networks. It is based on…

This article describe about Information Bottleneck Method, which is a technique in information theory for finding the best tradeoff between accuracy and complexity. Optimal temporal…

Expectation Maximization Algorithm is a natural generalization of maximum likelihood estimation to the incomplete data case. It is used to find maximum likelihood parameters of a…

This article talks about Hidden Markov Model, which are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech…

Decision Tree Learning is one of the most successful techniques for supervised classification learning. It is one of the most widely used and practical methods…

Perceptron is intended to be a machine, rather than a program, and while its first implementation was in software for the IBM 704, it is…

Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables in a learning problem. Naive Bayes Classifier technique…