ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequence outlier detection based on chaos theory and its application on stock market
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Some connectivity based cluster validity indices
Applied Soft Computing
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Abstract: Many of the existing network theory based artificial immune systems have been applied to data clustering. The formation of artificial lymphocyte (ALC) networks represents potential clusters in the data. Although these models do not require any user specified parameter of the number of required clusters to cluster the data, these models do have a drawback in the techniques used to determine the number of ALC networks. This paper discusses the drawbacks of these techniques and proposes two alternative techniques which can be used with the local network neighbourhood artificial immune system. The end result is an enhanced model that can dynamically determine the number of clusters in a data set.