Open AccessJournal Article10.1243/0954406041319509

An Incremental K-means algorithm

- 01 Jul 2004

- Vol. 218, Iss: 7, pp 783-795

108

TL;DR: An incremental version of the K-means algorithm that is also based on distortion reduction through cluster centre movement but is not so sensitive to inaccuracies in distortion estimation is proposed in this paper.

Abstract: Data clustering is an important data exploration technique with many applications in engineering, including parts family formation in group technology and segmentation in image processing. One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency. The main problem with this clustering method is its tendency to coverge at a local minimum. In this paper, the cause of this problem is explained and an existing solution involving a cluster centre jumping operation is examined. The jumping technique alleviates the problem with local minima by enabling cluster centres to move in such a radical way as to reduce the overall cluster distortion. However, the method is very sensitive to errors in estimating distortion. A clustering scheme that is also based on distortion reduction through cluster centre movement but is not so sensitive to inaccuracies in distortion estimation is proposed in this paper. The scheme, which is an incremental versi...

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1. What are the contributions in "An incremental k-means algorithm" ?

In this paper, the cause of this problem is explained and an existing solution involving a cluster centre jumping operation is examined.. A clustering scheme that is also based on distortion reduction through cluster centre movement but is not so sensitive to inaccuracies in distortion estimation is proposed in this paper.. The paper presents test results to demonstrate the efficacy of the proposed algorithm.

2. What are the future works in "An incremental k-means algorithm" ?

Thus, the algorithm has to investigate all possibilities in order to find the most beneficial place to insert the new cluster.

Fig. 5 Comparison of the running time of K-means, Incremental K-means and Incremental K-means with termination conditions

Fig. 3 The object distribution of the contrived datasets

Fig. 1 The results of applying K-means ðK ¼ 4Þ on two split regions

Citations

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References

Some methods for classification and analysis of multivariate observations

James B. MacQueen

- 01 Jan 1967

TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.

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Journal Article•10.1109/TPAMI.2002.1017616

An efficient k-means clustering algorithm: analysis and implementation

Tapas Kanungo, +5 more

- 01 Jul 2002

- IEEE Transactions on Pattern Analysis an...

TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.

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6.2K

•Journal Article•10.1016/S0031-3203(02)00060-2

The global k-means clustering algorithm

Aristidis Likas, +2 more

- 01 Feb 2003

- Pattern Recognition

TL;DR: The global k-means algorithm is presented which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N executions of the k-Means algorithm from suitable initial positions.

...read moreread less

3.6K

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An Incremental K-means algorithm

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Citations

Selection of K in K-means clustering:

A modeler's guide to handle complexity in energy systems optimization

Clustering techniques and their applications in engineering

Iterative big data clustering algorithms: a review

A Review and Characterization of Progressive Visual Analytics

References

Some methods for classification and analysis of multivariate observations

UCI Repository of machine learning databases

Finding Groups in Data

An efficient k-means clustering algorithm: analysis and implementation

The global k-means clustering algorithm

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