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Apache Mahout

Apache Mahout. Qiaodi Zhuang Xijing Zhang. What is Mahout?. Mahout is a scalable machine learning library from Apache. It uses MapReduce paradigm which in combination with Hadoop can be used as an inexpensive solution to solve machine learning problems.

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Apache Mahout

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  1. Apache Mahout Qiaodi Zhuang Xijing Zhang

  2. What is Mahout? • Mahout is a scalablemachine learning library from Apache. • It uses MapReduceparadigm which in combination with Hadoop can be used as an inexpensive solution to solve machine learning problems. • [1].Anil, Robin, Ted Dunning, and Ellen Friedman. Mahout in action. Manning, 2011.

  3. Problem&Challenge • Many datasets now are: • Far too large for a single machine, cannot fit into main memory • [2].http://www.orzota.com/apache-mahout-and-machine-learning/

  4. Mahout’s Algorithms: • Clustering: Kmeans, Fuzzy Kmeans • Classification: SVM, Random Forests • Recommender • Pattern Mining • Regression

  5. K-means Algorithms: • Input: a database D, of m records, r1, ..., rm and a desired number of clusters k • Output: set of k clusters that minimizes the squared error criterion • Begin • Randomly choose k records as the centroids for the k clusters; • repeatassign each record ri to a cluster such that the distance between ri • and the cluster centroid (mean) is the smallest among the k clusters; recalculate the centroid (mean) for each cluster based on the records • assigned to the cluster; • until no change; • End;

  6. K-means Clustering in Mahout • [3].K-means Clustering in the Cloud -- A Mahout Test, R. M. Esteves et al.,IEEE Advanced Information Networking and Applications , 2011,

  7. Evaluation The dataset is from the 1999 KDD cup. It has 4,940,000 records, with 41 attributes and 1 label (converted to numerical. A 1.1 GB dataset was used. This file was randomly segmented into smaller files. • [3].K-means Clustering in the Cloud -- A Mahout Test, R. M. Esteves et al.,IEEE Advanced Information Networking and Applications , 2011,

  8. [3].K-means Clustering in the Cloud -- A Mahout Test, R. M. Esteves et al.,IEEE Advanced Information Networking and Applications , 2011,

  9. Future • Classification • Decision Trees such as J48 and ID3 • Clustering • DBSCAN and CoWeb Clustering techniques • Association Rules • Apriori

  10. References: • [1].Anil, Robin, Ted Dunning, and Ellen Friedman. Mahout in action. Manning, 2011. • [2].http://www.orzota.com/apache-mahout-and-machine-learning/ • [3].K-means Clustering in the Cloud -- A Mahout Test, R. M. Esteves et al.,IEEE Advanced Information Networking and Applications , 2011, • [4].https://mahout.apache.org/ • [5].http://www.ibm.com/developerworks/java/library/j-mahout/

  11. Question?

  12. Thank you!

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