pddp2means: README

 Changes to be committed:
	new file:   README.md

pddp2means/README

-/***********************************************************************************************
-*cr                                       University of Patras, Greece
-*cr                                   Copyright (c) 2015 University of Patras
-*cr                                           All rights reserved
-*cr
-*cr                                           Developed by: HPClab 
-*cr                               Computer Engineering and Informatics Department
-*cr                                           University of Patras
-*cr
-***********************************************************************************************/
-
-Usage: 
-```
-#./pddp_2means <input_file> <output_file> <clusters>
-./pddp_2means ../data/40k.csv pddp_2means.out 19
-```

pddp2means/README.md

+=======
+README
+=======
+
+# 1. Code sample name
+pddp2means
+
+# 2. Description of the code sample package
+Clustering is the task of grouping a set of objects in such way
+that objects assigned in the same group exhibit greater similarity
+than those located, according to some computable criterion of
+similarity. Our approach on designing a parallel clustering algorithm optimized for the Intel Xeon-Phi
+coprocessor.
+
+Our implementation is based on the Principal Direction Divisive Partitioning (PDDP) 2-means algorithm. 
+
+The steps of the implementation include:
+
+1. Create a binary tree.
+2. Find a splittable leaf with the greatest scatter value to use pddp 2 means algorithm on.
+3. Find an approximation of the dominant eigenvector v of the matrix leaf(data)–leaf(centroid) using the power iteration algorithm.
+4. Use the values of vector v to initialize the 2 means algorithm by creating a first set of clusters.
+5. Use the result of 2-means to split the cluster of the leaf parent into two new clusters one for each leaf child.
+6. Repeat steps 2. to 5. until you have the amount of clusters wanted.
+
+This algorithm provided very stable and accurate solutions for the clustering problem (in terms of the Dunn Index metric).
+
+Additional pre-requisites:
+* Intel Compiles Suite
+* Intel Xeon Phi
+
+# 3. Release date
+25 January 2015
+
+# 4. Version history 
+1.0
+
+# 5. Contributor (s) / Maintainer(s) 
+Nikos Nikoloutsakos <nikoloutsa@admin.grnet.gr>
+
+# 6. Copyright / License of the code sample
+Apache 2.0
+
+# 7. Language(s) 
+C
+
+# 8. Parallelisation Implementation(s)
+OpenMP
+Intel Xeon Phi - Offload Mode
+
+# 9. Level of the code sample complexity 
+Sample data example demonstrating the use of pddp-2means clustering in a small data set.
+
+# 10. Instructions on how to compile the code
+Use the Makefile included in the src/ directory.
+
+# 11. Instructions on how to run the code
+./pddp_2means <input_file> <output_file> <clusters>
+
+# 12. Sample input(s)
+Input-data is included in the data/ folder
+
+# 13. Sample output(s)
+Output the membership of the data point to the cluster.