Arboricity and bipartite subgraph listing algorithms
Information Processing Letters
A comparison of some contextual discretization methods
Information Sciences: an International Journal
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index
Neural Processing Letters
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A New Conceptual Clustering Framework
Machine Learning
Mining Cross-Graph Quasi-Cliques in Gene Expression and Protein Interaction Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Consensus algorithms for the generation of all maximal bicliques
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
Information Sciences: an International Journal
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
A clustering method to identify representative financial ratios
Information Sciences: an International Journal
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Mining frequent cross-graph quasi-cliques
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Transactions on Knowledge Discovery from Data (TKDD)
Statistical Analysis and Data Mining
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
A correspondence between maximal complete bipartite subgraphs and closed patterns
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Efficient mining of large maximal bicliques
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Mining a new fault-tolerant pattern type as an alternative to formal concept discovery
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Information Sciences: an International Journal
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
Information Sciences: an International Journal
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Stocks with similar financial ratio values across years have similar price movements. We investigate this hypothesis by clustering groups of stocks that exhibit homogeneous financial ratio values across years, and then study their price movements. We propose using cross-graph quasi-biclique (CGQB) subgraphs to cluster stocks, as they can define the three dimensional (3D) subspaces of financial ratios that the stocks are homogeneous in across the years, and they can also handle missing values that are rampant in the stock data. Furthermore, investors can easily analyze these 3D subspaces to explore the relations between the stocks and financial ratios. We develop a novel algorithm, CGQBminer, which mines the complete set of CGQB subgraphs from the stock data. Through experimental analysis, we show that the hypothesis is valid. Furthermore, we demonstrate that having an investment strategy which uses groups of stocks mined by CGQB subgraphs have higher returns than one that does not. We also conducted an extensive performance analysis on CGQBminer, and show that it is efficient across different 3D datasets and parameter settings.