Algorithms for clustering data
Algorithms for clustering data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Novel Hybrid Hierarchical-K-means Clustering Method (H-K-means) for Microarray Analysis
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Rule Clustering and Super-rule Generation for Transmembrane Segments Prediction
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Statistical methods for automated generation of service engagement staffing plans
IBM Journal of Research and Development - Business optimization
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
A sampling approach for protein backbone fragment conformations
International Journal of Data Mining and Bioinformatics
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Many algorithms or techniques to discover motifs require a predefined fixed window size in advance. Because of the fixed size, these approaches often deliver a number of similar motifs simply shifted by some bases or including mismatches. To confront the mismatched motifs problem, we use the super-rule concept to construct a Super-Rule-Tree (SRT) by a modified Hybrid Hierarchical K-means (HHK) clustering algorithm, which requires no parameter set-up to identify the similarities and dissimilarities between the motifs. By analysing the motif results generated by our approach, they are significant not only in sequence area but also in secondary structure similarity.