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Abstract

A method of mining frequent items in data is described. Categorical associations between elements of data are the core of information contained in the data and are all that is needed to perform data mining. These associations are extracted from data and held in optimized associative matrices whose structure is independent of the nature and structure of the data. All data mining operations and discoveries can be performed using only these associative matrices which provides many advantages over present methods. It allows real-time interactive navigation through the information in the data, enables efficient automatic and user guided determination of the most highly correlated data components, and a winnowing navigation through a large number of automatically determined associations, as for example frequent item sets, amongst which the needle-in-the-haystack may be more easily found.

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First Claim