Data Mining - Clustering (Function|Model)

Thomas Bayes

About

Clustering

To identify natural groupings in the data.

Useful for exploring data and finding natural groupings within the data.

Members of a cluster are more like each other than they are like members of a different cluster.

The process of clustering is really a process of choosing a good partition of the data.

Algorithm

  1. K Means
  2. O Cluster

Type

Function

Finds natural groupings in the data

Model

Clustering models use descriptive data mining techniques, but they can be applied to classify cases according to their cluster assignments.

The model defines segments, or “clusters” of a population, then decides the likely cluster membership of each new case.

Example

  • A model might identify the segment of the population that has an income within a specified range, that has a good driving record, and that leases a new car on a yearly basis.
  • Segment demographic data into clusters and rank the probability that an individual will belong to a given cluster
  • Common examples include finding new customer segments, and life sciences discovery.




Discover More
Division
Data Mining - (Descriptive|Discovery) (Analysis|Statistics)

Descriptive analysis is also known as Descriptive statistics They are procedures used to summarize, organize, and simplify data. Descriptive function are always unsupervised See also . Visual...
Data Mining Algorithm
Data Mining - Algorithms

An is a mathematical procedure for solving a specific kind of problem. For some data mining functions, you can choose among several algorithms. Algorithm Function Type Description Decision...
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Data Mining - Orthogonal Partitioning Clustering (O-Cluster or OC) algorithm

O-Cluster creates a hierarchical, grid-based clustering model. This Unsupervised algorithm creates clusters that define dense areas in the attribute space. A sensitivity parameter defines the baseline...
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Data Mining - Scoring (Applying)

The process of applying a model to new data is known as scoring. Apply data, also called scoring data, is the actual population to which a model is applied. Scoring operation for: classification,...



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