Future Generation Computer Systems - Special double issue on data mining
Clustering Algorithms
Extending K-Means Clustering to First-Order Representations
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Grid-Based knowledge discovery in clinico-genomic data
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Supporting clinico-genomic knowledge discovery: a multi-strategy data mining process
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Acquisition of concept descriptions by conceptual clustering
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
A modification of the k-means method for quasi-unsupervised learning
Knowledge-Based Systems
Multi-level clustering support vector machine trees for improved protein local structure prediction
International Journal of Data Mining and Bioinformatics
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Efficient partitioning of large data sets into homogeneous clusters is fundamental problem in data mining. The hierarchical clustering methods are not adaptable because of their high computational complexity. The K-means based algorithms give promising results for their efficiency. However their use in often limited to numeric data. The quality of clusters produced depends on the initialization of clusters and the order in which is based on the K-means philosophy but removes the numeric data limitation.