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+---
+title: Clustering Algorithms
+---
+## Clustering Algorithms
+
+Clustering is the process of dividing data into separated groups (clusters), while ensuring that:
+- Each cluster contains similar objects
+- Objects which do not belong to the same clusters are not similar
+
+The difficulty lies in the definition of a similarity measure that can separate the data in the way we want. For instance, a group of persons can be separated by gender, hair color, weight, race...etc.
+
+Clustering algorithms have the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
+
+## Types of Clustering Algorithms:
+1. Connectivity-based clustering (hierarchical clustering)
+2. Centroid-based clustering (k-means clustering)
+3. Distribution-based clustering 
+4. Density-based clustering 
+
+## Examples 
+1. Alogmerative clustering
+2. k-means clustering
+3. k-mediods clustering
+4. Partition Clustering
+
+### K-Means Clustering
+
+K-means algorithm is a popular clustering algorithm since it is relatively simple and fast, as opposed to other clustering algorithms. The algorithm is defined as the following:
+
+1. Decide input parameter k (number of clusters)
+2. Pick k random data points to use as centroids
+3. Compute distances for all data points to each k centroids, and assign each data point to a cluster containing the closest centroid
+4. Once all data points have been classified, compute the midpoint of all points for each cluster and assign as new centroid
+5. Repeat steps 3 and 4 until the centroids converge upon certain k points.
+
+
+Since we only need to calculate k x n distances (rather than n(n-1) distances for knn algorithm), this algorithm is quite scalable.
+
+Let's cluster on the Iris Dataset https://www.kaggle.com/uciml/iris
+
+```python
+import pandas as pd
+import numpy as np
+iris = pd.read_csv('Iris.csv')
+del iris['Id']
+del iris['SepalLengthCm']
+del iris['SepalWidthCm']
+
+from matplotlib import pyplot as plt
+# k is the input parameter set to the number of species
+k = len(iris['Species'].unique())
+for i in iris['Species'].unique():
+    # select only the applicable rows
+    ds = iris[iris['Species'] == i]
+    # plot the points
+    plt.plot(ds[['PetalLengthCm']],ds[['PetalWidthCm']],'o')
+plt.title("Original Iris by Species")
+plt.show()
+
+from sklearn import cluster
+del iris['Species']
+kmeans = cluster.KMeans(n_clusters=k, n_init=10, max_iter=300, algorithm='auto')
+kmeans.fit(iris)
+labels = kmeans.labels_
+centroids = kmeans.cluster_centers_
+
+for i in range(k):
+    # select only data observations from the applicable cluster
+    ds = iris.iloc[np.where(labels==i)]
+    # plot the data observations
+    plt.plot(ds['PetalLengthCm'],ds['PetalWidthCm'],'o')
+    # plot the centroids
+    lines = plt.plot(centroids[i,0],centroids[i,1],'kx')
+    # make the centroid x's bigger
+    plt.setp(lines,ms=15.0)
+    plt.setp(lines,mew=2.0)
+plt.title("Iris by K-Means Clustering")
+plt.show()
+```
+
+Since the data points belong usually to a high-dimensional space, the similarity measure is often defined as a distance between two vectors (Euclidean, Manhathan, Cosine, Mahalanobis...)
+
+#### More Information:
+<!-- Please add any articles you think might be helpful to read before writing the article -->
+* [Cluster Analysis](https://en.wikipedia.org/wiki/Cluster_analysis)
+* [Introduction to Clustering and related algorithms](https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/)
+* https://www.datascience.com/blog/k-means-clustering
+* http://fromdatawithlove.thegovans.us/2013/05/clustering-using-scikit-learn.html
+* http://datascienceis.life