From f46c81d7287ff847c2305a5a2940b3e5083e53bb Mon Sep 17 00:00:00 2001
From: Abhijeet <abhijeetkislay@gmail.com>
Date: Mon, 24 Mar 2014 08:31:56 +0530
Subject: [PATCH] abstract revised for KMeans.ipynb

---
 doc/ipython-notebooks/clustering/KMeans.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/doc/ipython-notebooks/clustering/KMeans.ipynb b/doc/ipython-notebooks/clustering/KMeans.ipynb
index c85cdaf68cf..d69ba354bf4 100644
--- a/doc/ipython-notebooks/clustering/KMeans.ipynb
+++ b/doc/ipython-notebooks/clustering/KMeans.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:78cbbd3e39a5fb064f5d9a205d2b94648abdd5b6779156f48c941f69530400f4"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -27,7 +28,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In this notebook we are going to see how Shogun Machine Learning Toolbox can be used for clustering with KMeans. In particular, we will be discussing the various options/choices provided to a user by the KMeans implementation in Shogun. "
+      "This notebook demonstrates <a href=\"http://en.wikipedia.org/wiki/K-means_clustering\">clustering with KMeans</a> in shogun along with it's initialization and training. The initialization of cluster centres is shown manually, randomly and that from using <a href=\"http://en.wikipedia.org/wiki/K-means%2B%2B\">KMeans++</a> algorithm. The training is shown using Classical <a href=\"http://en.wikipedia.org/wiki/Lloyd%27s_algorithm\">Lloyd</a> and mini-batch Kmeans method. Finally it is worked upon a Real dataset, further adding <a href=\"http://en.wikipedia.org/wiki/Principal_component_analysis\">PCA</a> to to its cause."
      ]
     },
     {