Business Statistics

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…

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…

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…

Multinomial Logistic Regression is used to describe data and to explain the relationship between one dependent nominal variable and one or more continuous-level independent variables.…

Logistic Regression is used to describe data and to explain the relationship between one dependent binary variable and one or more metric independent variables.It measures…

This article talks about Linear Discriminant Analysis (LDA), which is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine…

This article briefly describe on Linear Classifier. The Linear Classifier models for classification separate input vectors into classes using linear decision boundaries. Linear Classifier achieved…