OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Towards interactive exploration of gene expression patterns
ACM SIGKDD Explorations Newsletter
Mining coherent gene clusters from gene-sample-time microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
An Interactive Approach to Mining Gene Expression Data
IEEE Transactions on Knowledge and Data Engineering
GPX: interactive mining of gene expression data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Cluster analysis on time series gene expression data
International Journal of Business Intelligence and Data Mining
Identifying synchronous and asynchronous co-regulations from time series gene expression data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Data analysis and bioinformatics
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Pattern recognition in biological time series
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
SciQL: bridging the gap between science and relational DBMS
Proceedings of the 15th Symposium on International Database Engineering & Applications
A general approach to mining quality pattern-based clusters from microarray data
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
International Journal of Data Warehousing and Mining
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Discovering coherent gene expression patterns in time-series gene expression data is an important task in bioinformatics research and biomedical applications. In this paper, we propose an interactive exploration framework for mining coherent expression patterns in time-series gene expression data. We develop a novel tool, coherent pattern index graph, to give users highly confident indications of the existences of coherent patterns. To derive a coherent pattern index graph, we devise an attraction tree structure to record the genes in the data set and summarize the information needed for the interactive exploration. We present fast and scalable algorithms to construct attraction trees and coherent pattern index graphs from gene expression data sets. We conduct an extensive performance study on some real data sets to verify our design. The experimental results strongly show that our approach is more effective than the state-of-the-art methods in mining real gene expression data, and is scalable in mining large data sets.