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Oracle® Data Mining Concepts
11g Release 2 (11.2)

Part Number E16808-03
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13 k-Means

This chapter describes the enhanced k-Means clustering algorithm supported by Oracle Data Mining.

See Also:

Chapter 7, "Clustering"

This chapter includes the following topics:

About k-Means

The k-Means algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters (provided there are enough distinct cases).

Distance-based algorithms rely on a distance metric (function) to measure the similarity between data points. The distance metric is either Euclidean, Cosine, or Fast Cosine distance. Data points are assigned to the nearest cluster according to the distance metric used.

Oracle Data Mining implements an enhanced version of the k-means algorithm with the following features:

This approach to k-means avoids the need for building multiple k-means models and provides clustering results that are consistently superior to the traditional k-means.

The clusters discovered by enhanced k-Means are used to generate a Bayesian probability model that is then used during scoring (model apply) for assigning data points to clusters. The k-means algorithm can be interpreted as a mixture model where the mixture components are spherical multivariate normal distributions with the same variance for all components.

Data Preparation for k-Means

Automatic Data Preparation performs outlier-sensitive normalization for k-Means.

When there are missing values in columns with simple data types (not nested), k-Means interprets them as missing at random. The algorithm replaces missing categorical values with the mode and missing numerical values with the mean.

When there are missing values in nested columns, k-Means interprets them as sparse. The algorithm replaces sparse numerical data with zeros and sparse categorical data with zero vectors.

If you manage your own data preparation for k-Means, keep in mind that outliers with equi-width binning can prevent k-Means from creating clusters that are different in content. The clusters may have very similar centroids, histograms, and rules.