MineBench: A Benchmark Suite for Data Mining Workloads. Data mining constitutes an important class of scientific and commercial applications. Recent advances in data extraction techniques have created vast data sets, which require increasingly complex data mining algorithms to sift through them to generate meaningful information. The disproportionately slower rate of growth of computer systems has led to a sizeable performance gap between data mining systems and algorithms. The first step in closing this gap is to analyze these algorithms and understand their bottlenecks. With this knowledge, current computer architectures can be optimized for data mining applications. In this paper, we present MineBench, a publicly available benchmark suite containing fifteen representative data mining applications belonging to various categories such as clustering, classification, and association rule mining. We believe that MineBench will be of use to those looking to characterize and accelerate data mining workloads.

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  1. Zihayat, Morteza; Chen, Yan; An, Aijun: Memory-adaptive high utility sequential pattern mining over data streams (2017)
  2. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
  3. Zihayat, Morteza; An, Aijun: Mining top-(k) high utility patterns over data streams (2014)
  4. Lin, Ming-Yen; Tu, Tzer-Fu; Hsueh, Sue-Chen: High utility pattern mining using the maximal itemset property and lexicographic tree structures (2012) ioport
  5. Luo, Chunjie; Zhan, Jianfeng; Jia, Zhen; Wang, Lei; Lu, Gang: Cloudrank-D: benchmarking and ranking cloud computing systems for data processing applications (2012) ioport
  6. Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Choi, Ho-Jin: A framework for mining interesting high utility patterns with a strong frequency affinity (2011) ioport
  7. Harmanci, Derin; Gramoli, Vincent; Felber, Pascal; Fetzer, Christof: Extensible transactional memory testbed (2010)
  8. Redaelli, F.; Santambrogio, M. D.; Memik, S. Ogrenci: An ILP formulation for the task graph scheduling problem tailored to bi-dimensional reconfigurable architectures (2009) ioport
  9. Che, Shuai; Boyer, Michael; Meng, Jiayuan; Tarjan, David; Sheaffer, Jeremy W.; Skadron, Kevin: A performance study of general-purpose applications on graphics processors using CUDA (2008) ioport