Learning Associations

Learning associations is a method for discovering interesting relations between variables in large databases. It is used to identify regularities among large scale databases. The concept of learning association is mainly used in market analysis which is finding association between products bought by customers. If people who buy “x” typically also buy “y”, and if there is a customer who buys “x” and does not buy “y”, he or she is a potential customer. Once such customers are identified, they can be targeted for cross selling.


Classification is the problem of identifying to which of a set of categories a new observation to which of a set of categories a new observation belong on the basis of a training set of data containing observation whose category membership is known.

Pattern Recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of similarities and regularities in data.

Natural Language Processing

Natural language processing is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages.


Biometrics is recognition or authentications of people using their physiological and/or behavioral characteristics that requires an integration of inputs from different modalities.

Knowledge Extraction

Learning a rule from data is known as knowledge extraction. The rule is a simple model that explains the data, using which an explanation about the process underlying the data is generated.


By fitting a rule to the data an explanation that is simpler than the data, requiring less memory to store and less computation to process can be generated. This process is known as Compression.

Outlier Detection

Outlier detection is the process of finding the instances that do not obey the rule and are exceptions to the standard case.


Regression analysis is a statistical process for estimating the relationships among variables.

Density Estimation

There is a structure to the input space such that certain patterns occur more often than others, and based on the frequency of occurrence, it has to be analyzed that what happens and what does not. One type of density estimation is clustering.

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