Software Fault Feature Clustering Algorithm Based on Sequence Pattern
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Soft topographic map for clustering and classification of bacteria
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Approaching process mining with sequence clustering: experiments and findings
BPM'07 Proceedings of the 5th international conference on Business process management
Soft topographic maps for clustering and classifying bacteria using housekeeping genes
Advances in Artificial Neural Systems
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Protein sequence clustering has been widely used as a part of the analysis of protein structure and function. In most cases single link or graph-based clustering algorithms have been applied. In this paper, we demonstrate an approach of clustering proteins, SEQOPTICS (sequence clustering with OPTICS), which is based on OPTICS (Ordering Points To Identify the Clustering Structure), an attractive approach due to its emphasis on visualization of results and support for interactive work, e.g., in choosing parameters. OPTICS has not been used, as far as we know, for protein sequence clustering. We have implemented a system with OPTICS at its core to perform protein sequence clustering. In this paper, we test SEQOPTICS with four data sets from different data sources. Visualization of the sequence clustering structure is demonstrated. Our system was evaluated by comparison with other existing methods. Analysis of the results demonstrates that our system perform better by the Jaccard coefficient evaluation criterion.