**Introduction :**

The goal of the blogpost is to get the beginners started with fundamental concepts of the K Means clustering Algorithm. We will mainly focus on learning to build your first K Means clustering model. The data cleaning and preprocessing parts would be covered in detail in an upcoming post.

**Clustering :**

Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. We can show this with a simple graphical example:

In this case we easily identify the 4 clusters into which the data can be divided; the similarity criterion is distance: two or more objects belong to the same cluster if they are “close” according to a given distance (in this case geometrical distance). This is called distance-based clustering. Another kind of clustering is conceptual clustering: two or more objects belong to the same cluster if this one defines a concept common to all that objects. In other words, objects are grouped according to their fit to descriptive concepts, not according to simple similarity measures.

**The Goals of Clustering**

So, the goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? It can be shown that there is no absolute “best” criterion which would be independent of the final aim of the clustering. Consequently, it is the user which must supply this criterion, in such a way that the result of the clustering will suit their needs. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection).

**K-Means **

K-Means is one of the most popular “clustering” algorithms. K-means stores k centroids that it uses to define clusters. A point is considered to be in a particular cluster if it is closer to that cluster’s centroid than any other centroid.

K-Means finds the best centroids by alternating between (1) assigning data points to clusters based on the current centroids (2) choosing centroids (points which are the center of a cluster) based on the current assignment of data points to clusters.

**How it works :**

**Input**: k(the number of clusters),

D(a set of lift ratios)

**Output**: a set of k clusters

**Method**:

Arbitrarily choose k objects from D as the initial cluster centers;

**Repeat**:

1.(re)assign each object to the cluster to which the object is the most similar, based on the mean value of the objects in the cluster;

2. Update the cluster means, i.e., calculate the mean value of the objects for each cluster

**Until** no change;

The distance metric used to calculate similarity in step 1 is Euclidean distance

**Euclidean distance**

Euclidean distance is the most commonly used distance measure. Euclidean distance also called as simply distance. The usage of Euclidean distance measure is highly recommended when data is dense or continuous. Euclidean distance is the best proximity measure. The Euclidean distance between two points is the length of the path connecting them.The Pythagorean theorem gives this distance between two points. A generalized term for the Euclidean norm is the L2 norm or L2 distance.