What types of data structure are widely used in cluster algorithm? Explain DATA MATRIX AND DISSIMILARITY MATRIX

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 DATA MATRIX AND DISSIMILARITY MATRIX

First of all, let us know what types of data structures are widely used in cluster analysis. Main memory-based clustering algorithms typically operate on either of the following two data structures.

 Data Matrix: This represents n objects, such as persons, with m attributes, such as age, height. weight, gender, race, and so on. The structure is in the form of a relational table, or n-by-m matrix (n objects x m attributes). The Data Matrix is often called a two-mode matrix since the rows represent objects and columns represent attributes.



Dissimilarity Matrix: This stores a collection of proximities that are available for all pairs of n objects. It is often represented by an n-by-n matrix, where dü, i) is the measured difference or dissimilarity between objects i and J. In general, d(i, i) is a non-negative number that is close to 0 when objects i and j are highly similar or near to each other and become larger the more they differ. The distance measure is symmetric in nature that is, d(i ) -d (j), i) and the distance of an object from itself is zero that is, d(i, i) "0, we have the matrix in the figure.



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