DBSCAN-Clustering-using-Python

In Unsupervised Learning we have different type of algorithms such as:

1. Clustering
2. Association Rules
3. Recommendation Engine
4. PCA
5. Text mining
6. NLP

In Clustering we have :

1. Hierarchial Clustering
2. K-Means Clustering
3. DBSCAN Clustering

In this repository we will discuss mainly about DBSCAN Clustering

There are some disadvantages in Hierarchial clustering and K - means Clustering, among them main disadvantages are that they doesnt perform well with non-spherical shapes of clusters and sensitive to noise or outliers.

To deal with this we have Density Based Spatial Clustering (DBSCAN) :

    -It is mainly used to find outliers and merge them and to deal with non-spherical data
    -Clustering is mainly done based on density of data points (where more number of data points are present).

step 1: Mainly we have 2 parameters:

        1. eps
        2. Min points

step 2: eps >0 => compulsary

        Suppose eps=2 which means it randomly chooses a point x from that point it draws a circle with radius=2 that is nothing but cluster with eps=2

step 3: Min points, to know neighbourhood is dense enough or not we use min points.

        Suppose minpoints = 8, which means neighbourhood has atleast >= 8 data points then it is denser and forms cluster, if < 8 data points then not denser and outliers.

step 4: Border points

        In eps neighbourhood that contains less than min points but it belongs to eps neighbourhood of another core point

Note:
If not core point and not border point then it is noise or outliers

Data Used:

      wholesale customers data: depending on their priorities clusters of customers are formed

Programming:

      Python

The Codes regarding this DBSCAN Clustering with different business problem Clusturing of customers depending on their priorities with its dataset is present in this Repository in detail