OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Gene Expression Data Mining for Functional Genomics using Fuzzy Technology
Advances in Computational Intelligence and Learning: Methods and Applications
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Discovering Gene Networks with a Neural-Genetic Hybrid
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
A new fuzzy clustering algorithm for optimally finding granular prototypes
International Journal of Approximate Reasoning
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
Cluster analysis of gene expression data based on self-splitting and merging competitive learning
IEEE Transactions on Information Technology in Biomedicine
An evolutionary approach for gene expression patterns
IEEE Transactions on Information Technology in Biomedicine
Application of Simulated Annealing to the Biclustering of Gene Expression Data
IEEE Transactions on Information Technology in Biomedicine
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A min-max approach to fuzzy clustering, estimation, and identification
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Gene expression data generated by DNA microarray experiments provide a vast resource of medical diagnostic and disease understanding. Unfortunately, the large amount of data makes it hard, sometimes even impossible, to understand the correct behavior of genes. In this work, we develop a possibilistic approach for mining gene microarray data. Our model consists of two steps. In the first step, we use possibilistic clustering to partition the data into groups (or clusters). The optimal number of clusters is evaluated automatically from the data using the Information Entropy as a validity measure. In the second step, we select from each computed cluster the most representative genes and model them as a graph called a proximity graph. This set of graphs (or hyper-graph) will be used to predict the function of new and previously unknown genes. Experimental results using real-world data sets reveal a good performance and a high prediction accuracy of our model.