Description:

K-tree is a tree structured clustering algorithm. It is also refered to as a Tree Structured Vector Quantizer (TSVQ). The goal of cluster analysis is to group objects based on similarity. Each object in a K-tree is represented by an n-dimensional vector. All vectors in the tree must have the same number of dimensions. At the K-tree 0.1 release the only similarity measure for vectors is Euclidean distance.

The algorithm is a hybrid of the B+-tree and k-means algorithms. It uses a similar tree structure to the B+-tree and uses k-means to perform splits. The tree forms a nearest neighbour search tree. Unlike k-means the number of clusters does not need to be specified upfront. However, a tree order must be specified that restricts how many vectors can be stored in any node. Each level of the tree produces a different number of clusters.

Changes to previous version:

Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Ascii, Java Arrays

Tags: Clustering, Algorithm

Archive: download here

Other available revisons

Version	Changelog	Date
0.4.2	Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.	July 4, 2011, 06:01:59
0.1.1	Initial Announcement on mloss.org.	February 27, 2010, 02:41:46

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