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|Algorithm|Description|Link|
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|A-priori SON| Finding Frequent Itemsets: SON Algorithm by A-Priori algorithm in stage 1. The implementation include Savasere, Omiecinski, and Navathe (SON) algorithm as a class and an A-Priori algorithm in python class encapsulates all functions which implement by static functions to support Spark RDD to call. |[Source Code](https://github.com/Cheng-Lin-Li/Spark/blob/master/A-Priori_SON/A-Priori_SON.py)|
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|A-priori and SON| Finding Frequent Itemsets: SON Algorithm by A-Priori algorithm in stage 1. The implementation include Savasere, Omiecinski, and Navathe (SON) algorithm as a class and an A-Priori algorithm in python class encapsulates all functions which implement by static functions to support Spark RDD to call. |[Source Code](https://github.com/Cheng-Lin-Li/Spark/blob/master/A-Priori_SON/A-Priori_SON.py)|
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|ALS with UV Decomposition|An implementation of UV Decomposition in Alternating Least Squares (ALS) Algorithm by Spark. The task is to modify the parallel implementation of ALS (alternating least squares) algorithm in Spark, so that it takes a utility matrix as the input and process by UV decomposition, and output the root-mean-square deviation (RMSE) into standard output or a file after each iteration. The code for the algorithm is als.py under the <spark-2.1.0 installation directory>/examples/src/main/python.|[Source Code](https://github.com/Cheng-Lin-Li/Spark/blob/master/ALS/ALS.py)|
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|TF-IDF wiht K-Means|A similarity algorithm implementation of TF-IDF algorithm with cosin similarity implementation on spark platform as the measure of K-Means. The implementation of k-means is provided by Spark in examples/src/main/python/ml/kmeans_example.py. |[Source Code](https://github.com/Cheng-Lin-Li/Spark/blob/master/TF-IDF_KMeans/kmeans.py)|
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|Matrix Multiplication by Two Phases approach|Matrix Multiplication: Two Phases approach to deal with huge matrix multiplication on spark platform|[Source Code](https://github.com/Cheng-Lin-Li/Spark/blob/master/Matrix_Multiplication/TwoPhase_Matrix_Multiplication.py)|
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