The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Improved Ant-Based Clustering and Sorting
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index
Neural Processing Letters
Ant Colony Optimization
FARMER: finding interesting rule groups in microarray datasets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
Semantic mining and analysis of gene expression data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
An efficient ant colony optimization approach to attribute reduction in rough set theory
Pattern Recognition Letters
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
A novel ant-based clustering approach for document clustering
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
Constrained clustering via swarm intelligence
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.