[Tutorials and Libraries for Deep Learning]
    http://kuantinglai.blogspot.tw/2017/05/deep-learning-reading-list-tools.html
[Tutorials and Libraries for Data Mining]
  1. Data Mining: An Overview from a Database Perspective
  2. http://cs.nju.edu.cn/zhouzh/zhouzh.files/course/dm/reading/reading01/chen_tkde96.pdf
    Also study slides from Data Mining 3e , http://hanj.cs.illinois.edu/bk3/:
    . Ch 6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
    . Ch8. Classification: Basic Concepts
    . Ch10. Cluster Analysis: Basic Concepts and Methods
  3. Mining Frequent Patterns: Apriori
  4. http://rakesh.agrawal-family.com/papers/vldb94apriori.pdf
  5. Mining Frequent Patterns : FP-Growth
  6. http://dl.acm.org/citation.cfm?id=335372
  7. Classification: SVM
  8. http://www.cise.ufl.edu/class/cis4930sp11dtm/notes/intro_svm_new.pdf
    http://www.csie.ntu.edu.tw/~cjlin/libsvm/
  9. Classification: Neural Network / Deep learning (e.g. Google's open-source TensorFlow)
  10. https://www.tensorflow.org/
    https://github.com/aymericdamien/TensorFlow-Examples
    Study "0 - Prerequisite" to "3 - Neural Networks - Multilayer Perceptron".
    For this part, you may use easier "Python + Keras".
    Keras is a high-level framework for deep learning. You may select TensorFlow or Theano as the learning engine.
    https://keras.io/
  11. Classification: Neural Network / Deep learning (e.g. Google's open-source TensorFlow)
  12. Other tools:
    (1) Python + scikit-learn (very detailed documentation)
    http://scikit-learn.org/stable/
    (2) Java +Weka
    (3) R
[Some paper study]
  1. S. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, Oct 2010
  2. P. Berkhin, Grouping Multidimensional Data: Recent Advances in Clustering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, ch. A Survey of Clustering Data Mining Techniques, pp. 25-71.
  3. J. Cheng, Y. Ke, and W. Ng, "A survey on algorithms for mining frequent itemsets over data streams," Knowledge and Information Systems, vol. 16, no. 1, pp. 1-27, 2007.
  4. A. Guille, H. Hacid, C. Favre, and D. A. Zighed, "Information diffusion in online social networks: A survey," SIGMOD Rec., vol. 42, no. 2, pp. 17-28, Jul. 2013.
  5. J. Han, H. Cheng, D. Xin, and X. Yan, "Frequent pattern mining: current status and future directions," Data Mining and Knowledge Discovery, vol. 15, no. 1, pp. 55-86, 2007.
  6. S. B. Kotsiantis, "Supervised machine learning: A review of classification techniques," in Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. Amsterdam, The Netherlands, The Netherlands: IOS Press, 2007, pp. 3-24.
  7. X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, and D. Steinberg, "Top 10 algorithms in data mining," Knowledge and Information Systems, vol. 14, no. 1, pp. 1-37, 2007.
[Mining Frequent Patterns + GPU]
  1. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," Proc. 20th International Conference Very Large Data Bases, pp. 478-499, Sept. 1994.
  2. M.-S. Chen, J. Han, and P. S. Yu. "Data mining: An overview from a database perspective". IEEE Trans. Knowledge and Data Engineering, 8:866-883, 1996.
  3. J. Park, M.-S. Chen, and P. S. Yu. "Using A Hash-Based Method with Transaction Trimming for Mining Association Rules," IEEE Trans. On Knowledge and Data Eng., vol. 9, no. 5, pp. 813-825, Sept./Oct. 1997.
  4. J. Han, J. Pei, Y. Yin, R. Mao, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach" DMKD, Vol. 8, No. 1, Jan. 2004. (SIGMOD 2000)
  5. Jay Ayres, J. E. Gehrke, Tomi Yiu, and Jason Flannick. Sequential pattern mining using bitmaps. In SIGKDD, 2002.
  6. Zaki MJ, Gouda K (2003) Fast vertical mining using diffsets. In: Proc SIGKDD, pp 326-335
  7. W. Fang, K. K. Lau, M. Lu, X. Xiao, C. K. Lam, P. Y. Yang, B. He1, Q. Luo, P. V. Sander, and K. Yang, "Parallel Data Mining on Graphics Processors" Technical Rep. HKUST, 2008
  8. Fan Zhang,Yan Zhang, Jason D. Bakos , "Accelerating frequent itemset mining on graphics processing units" J. of Supercomputing, 2013
  9. D. Singh and C. K Reddy, "A survey on platforms for big data analytics", J. of Big Data, 2014
  10. Y.-Y. Jo, S.-W. Kim, and D.-H. Bae, "Efficient Sparse Matrix Multiplication on GPU for Large Social Network Analysis", CIKM 2015
  11. CUDA C Programming Guide, NVIDIA
  12. http://docs.nvidia.com/cuda/cuda-c-programming-guide/
    https://developer.nvidia.com/cuda-toolkit