Oracle® Data Mining Concepts 12c Release 1 (12.1) E1769215 


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This chapter describes clustering, the unsupervised mining function for discovering natural groupings in the data. For an overview of unsupervised data mining, see Chapter 3.
This chapter contains these topics:
Clustering analysis finds clusters of data objects that are similar in some sense to one another. The members of a cluster are more like each other than they are like members of other clusters. Different clusters can have members in common. The goal of clustering analysis is to find highquality clusters such that the intercluster similarity is low and the intracluster similarity is high.
Clustering, like classification, is used to segment the data. Unlike classification, clustering models segment data into groups that were not previously defined. Classification models segment data by assigning it to previouslydefined classes, which are specified in a target. Clustering models do not use a target.
Clustering is useful for exploring data. If there are many cases and no obvious groupings, clustering algorithms can be used to find natural groupings.
Clustering can serve as a useful datapreprocessing step to identify homogeneous groups on which to build supervised models.
Clustering can also be used for anomaly detection. Once the data has been segmented into clusters, you might find that some cases do not fit well into any clusters. These cases are anomalies or outliers.
There are several different approaches to the computation of clusters. Oracle Data Mining supports the following methods:
Densitybased — This type of clustering finds the underlying distribution of the data and estimates how areas of high density in the data correspond to peaks in the distribution. Highdensity areas are interpreted as clusters. Densitybased cluster estimation is probabilistic.
Distancebased — This type of clustering uses a distance metric to determine similarity between data objects. The distance metric measures the distance between actual cases in the cluster and the prototypical case for the cluster. The prototypical case is known as the centroid.
Gridbased — This type of clustering divides the input space into hyperrectangular cells and identifies adjacent highdensity cells to form clusters.
Although clustering is an unsupervised mining function, Oracle Data Mining supports the scoring operation for clustering. New data is scored probabilistically.
The clustering algorithms supported by Oracle Data Mining perform hierarchical clustering. The leaf clusters are the final clusters generated by the algorithm. Clusters higher up in the hierarchy are intermediate clusters.
Since known classes are not used in clustering, the interpretation of clusters can present difficulties. How do you know if the clusters can reliably be used for business decision making?
Oracle Data Mining clustering models support a high degree of model transparency. You can evaluate the model by examining information generated by the clustering algorithm: for example, the centroid of a distancebased cluster. Moreover, because the clustering process is hierarchical, you can evaluate the rules and other information related to each cluster's position in the hierarchy.
Oracle Data Mining supports these clustering algorithms:
Expectation Maximization
Expectation Maximization is a probabilistic, densityestimation clustering algorithm. See Chapter 12, "Expectation Maximization".
kMeans
kMeans is a distancebased clustering algorithm. Oracle Data Mining supports an enhanced version of kMeans. See Chapter 14, "kMeans".
Orthogonal Partitioning Clustering (OCluster)
OCluster is a proprietary, gridbased clustering algorithm. See Chapter 18, "OCluster".
Reference:
Campos, M.M., Milenova, B.L., "OCluster: Scalable Clustering of Large High Dimensional Data Sets", Oracle Data Mining Technologies, 10 Van De Graaff Drive, Burlington, MA 01803.
The main characteristics of the two algorithms are compared in Table 71.
Table 71 Clustering Algorithms Compared