[benchmarks] Add a container for MineBench

[rouvoy]

NU-MineBench is a data mining benchmark suite containing a mix of several representative data mining applications from different application domains. This benchmark is intended for use in computer architecture research, systems research, performance evaluation, and high-performance computing. The well-known applications assembled in this benchmark suite have been collected from research groups in industry and academia. The applications contain highly optimized versions of the data mining algorithms. Scalable versions of the applications are also provided. Such extensions were designed and implemented by developers at Northwestern University. Currently, the benchmark has applications with algorithms based on clustering, association rules, classification, bayesian network, pattern recognition, support vector machines and several other well known data mining methodologies. These applications are used in diverse fields like bioinformatics, network intrusion, customer relationship management, and marketing.
If you would like to contribute any well-known and stable application to our benchmark suite, please do not hesitate to contact us.

List of algorithms and applications

• Approximate Frequent Itemset Miner
• Apriori association rule mining
• Naive Bayesian Network data classifier
• BIRCH data clustering
• ECLAT association rule mining
• GeneNet, a DNA sequencing application using Bayesian network
• HOP, a density-based data clustering
• K-means and Fuzzy K-means data clustering
• Parallel ETI Mining
• PLSA (Parallel Linear Space Alignment)
• Recursive_Weak, Recursive_Weak_pp
• RSearch, a sequence database searching with RNA structure queries
• ScalParC decision-tree based data classification
• Semphy, a structure learning algorithm that is based on phylogenetic trees
• SNP (Single Nucleotide Polymorphisms) data classification
• SVM-RFE (Support Vector Machines - Recursive Feature Elimination) is a feature selection algorithm
• Utility mining, association rule-based mining algorithm