diff --git a/js/ml/kmeans.js b/js/ml/kmeans.js
new file mode 100644
index 0000000..ef01430
--- /dev/null
+++ b/js/ml/kmeans.js
@@ -0,0 +1,101 @@
+/**
+ * Created by joonkukang on 2014. 1. 16..
+ */
+var math = Require('ml/math')
+var Kmeans = module.exports;
+
+Kmeans.cluster = function(options) {
+    var data = options['data'];
+    var k = options['k'];
+    var distance = getDistanceFunction(options['distance']);
+    var epochs = options['epochs'];
+    var init_using_data = options['init_using_data'];
+    if(typeof init_using_data === "undefined");
+        init_using_data = true;
+    var means = getRandomMeans(data,k, init_using_data);
+
+    var epoch, i, j, l;
+    var clusters = [];
+    for(i=0 ; i<k ; i++)
+        clusters.push([]);
+
+    for(epoch=0 ; epoch<epochs ; epoch++) {
+        clusters = [];
+        for(i=0 ; i<k ; i++)
+            clusters.push([]);
+
+        // Find which centroid is the closest for each row
+        for(i=0 ; i<data.length ; i++) {
+            var bestmatch = 0;
+            for(j=0 ; j<k ; j++) {
+                if(distance(means[j],data[i]) < distance(means[bestmatch],data[i])) bestmatch = j;
+            }
+            clusters[bestmatch].push(i);
+        }
+
+        // Move the centroids to the average of their members
+        for(i=0 ; i<k ; i++) {
+            var avgs = [];
+            for(j=0 ; j<data[0].length ; j++)
+                avgs.push(0.0);
+            if(clusters[i].length > 0) {
+                for(j=0 ; j<clusters[i].length ; j++) {
+                    for(l=0 ; l<data[0].length ; l++) {
+                        avgs[l] += data[clusters[i][j]][l];
+                    }
+                }
+                for(j=0 ; j<data[0].length ; j++) {
+                    avgs[j] /= clusters[i].length;
+                }
+                means[i] = avgs;
+            }
+        }
+    }
+    return {
+        clusters : clusters,
+        means : means
+    };
+}
+
+var getRandomMeans = function(data,k, init_using_data) {
+    var clusters = [];
+    if(init_using_data) {
+        var cluster_index = math.range(data.length);
+        cluster_index = math.shuffle(cluster_index);
+        for(i=0 ; i<k ; i++) {
+            clusters.push(data[cluster_index[i]]);
+        }
+    } else {
+        var i,j;
+        var ranges = [];
+        for(i=0 ; i<data[0].length ; i++) {
+            var min = data[0][i] , max = data[0][i];
+            for(j=0 ; j<data.length ; j++) {
+                if(data[j][i] < min) min = data[j][i];
+                if(data[j][i] > max) max = data[j][i];
+            }
+            ranges.push([min,max]);
+        }
+        for(i=0 ; i<k ; i++) {
+            var cluster = [];
+            for(j=0 ; j<data[0].length;j++) {
+                cluster.push(Math.random() * (ranges[j][1] - ranges[j][0]) + ranges[j][0]);
+            }
+            clusters.push(cluster);
+        }
+    }
+    return clusters;
+}
+
+
+function getDistanceFunction(options) {
+    if(typeof options === 'undefined') {
+        return math.euclidean;
+    } else if (typeof options === 'function') {
+        return options;
+    } else if (options['type'] === 'euclidean') {
+        return math.euclidean;
+    } else if (options['type'] === 'pearson') {
+        return math.pearson;
+    }
+}