Clustering-Algorithms-ML

Implementation of Clustering Algorithms used in Machine Learning. Clustering is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different. In other words, we try to find homogeneous subgroups within the data such that data points in each cluster are as similar as possible according to a similarity measure such as euclidean-based distance or correlation-based distance. The decision of which similarity measure to use is application-specific.

Installations

    pip install pandas
    pip install numpy
    pip install matplotlib

S.N	Clustering Algorithm	Dataset Used
1.	K-Means Clustering	Mall_Customers.csv
2.	Hierarchial Clustering	Mall_Customers.csv

K-Means Clustering Algorithm

K-Means algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster.

Process:

Specify number of clusters K.
Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing.

Selection of right number of clusters:
One measurement is Within Cluster Sum of Squares (WCSS), which measures the squared average distance of all the points within a cluster to the cluster centroid. To calculate WCSS, you first find the Euclidean distance (see figure below) between a given point and the centroid to which it is assigned. You then iterate this process for all points in the cluster, and then sum the values for the cluster and divide by the number of points. Finally, you calculate the average across all clusters. This will give you the average WCSS.

Observation after application of WCSS

Steps Involved:

Import the Libraries.
Import the datasets.
Using the elbow method to determine the number of clusters.
Training the dataset on K-Means Model.
Visualising the clusters.

Observation:

Hierarchcial Clustering

Hierarchical clustering is another unsupervised learning algorithm that is used to group together the unlabeled data points having similar characteristics. Hierarchical clustering algorithms falls into following two categories.

Agglomerative hierarchical algorithms: In agglomerative hierarchical algorithms, each data point is treated as a single cluster and then successively merge or agglomerate (bottom-up approach) the pairs of clusters. The hierarchy of the clusters is represented as a dendrogram or tree structure.
Divisive hierarchical algorithms: On the other hand, in divisive hierarchical algorithms, all the data points are treated as one big cluster and the process of clustering involves dividing (Top-down approach) the one big cluster into various small clusters.

Process:

Step 1: Make each data point a single point cluster -> That forms N cluster.
Step 2: Take the two closest data points and make them one cluster -> That form N-1 clusters. Step 3: Take the two closest cluster and make them one cluster -> That forms N-2 cluster.
Step 4: Repeat Step 3 until there is only one clsuter.

Selection of right number of clusters
A dendrogram is a diagram that shows the hierarchical relationship between objects. It is most commonly created as an output from hierarchical clustering. The main use of a dendrogram is to work out the best way to allocate objects to clusters.

Steps Involved:

Import the Libraries.
Import the datasets.
Using the dendrogram to determine the number of clusters.
Training the dataset on K-Means Model.
Visualising the clusters.

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