![Step 1: Randomly we choose two centroids for two clusters
k1=(185,72)
k2=(170,56)
Step 2: Now using these centroids we compute Eucledian Distance 3rd point
ED=sqrt[(xo-xc)^2+(y0-yc)^2]
k1=sqrt[(168-185)^2+(60-72)^2]
k1=20.80
k2=sqrt[(168-170)^2+(60-56)^2]
k2=4.48
Therefore 3 belongs to k2
Step 3: Calculate new centroid values for k2
k2=[(170+168)/2 , (60+56)/2]
k2=(169,58)
Individual Height Weight
1 185 72
2 170 56
3 168 60
4 179 68
5 182 72](https://image.slidesharecdn.com/clustering-210208145943/85/Clustering-9-320.jpg)
Clustering
- 2. INTRODUCTION ď Clustering is an unsupervised learning method of data abstraction. ď The method of identifying similar groups of data in a dataset is called Clustering. ď It is basically a collection of objects on the basis of similarity and dissimilarity between them.
- 3. TYPES OF CLUSTERING ď Hard Clustering In hard clustering, each data point either belongs to a cluster completely or not. ď Soft Clustering Soft clustering is about grouping the data items such that an item can exists in multiple clusters.
- 4. CLUSTERING METHODS Density-Based Methods : ďThese method search the data space for areas of varied density of data points in the data space. Hierarchical Based Methods: ďIn this method, the clusters forms a tree-type structure based on the hierarchy ďNew clusters are formed using the previously formed one. ďIt is divided into two category ⢠Agglomerative ⢠Divisive
- 5. Partitioning Based Methods: ďThese methods partition the objects into k cluster and each partition forms one cluster. example :- K means Grid-Based Methods: ďIn this method, the data space is formulated into a finite number of cells that form a grid-like structure.
- 6. K Means Clustering ď It is an algorithm to group similar elements or data points to cluster. ď The number of groups or cluster is represented by k. ď It assumes that the object attribute forms a vector space based on features that are already provided.
- 7. K Means Clustering Algorithm Step 1: First we initialize k points, called means, randomly. Step 2:We categorize each item to its closest mean and we update the meanâs coordinates, which are the averages of the items categorized in that mean so far. Step 3: We repeat the process for a given number of iterations and at the end, we have our clusters.
- 8. Example of K-means Clustering Let us consider a table Individual Height Weight 1 185 72 2 170 56 3 168 60 4 179 68 5 182 72
- 9. Step 1: Randomly we choose two centroids for two clusters k1=(185,72) k2=(170,56) Step 2: Now using these centroids we compute Eucledian Distance 3rd point ED=sqrt[(xo-xc)^2+(y0-yc)^2] k1=sqrt[(168-185)^2+(60-72)^2] k1=20.80 k2=sqrt[(168-170)^2+(60-56)^2] k2=4.48 Therefore 3 belongs to k2 Step 3: Calculate new centroid values for k2 k2=[(170+168)/2 , (60+56)/2] k2=(169,58) Individual Height Weight 1 185 72 2 170 56 3 168 60 4 179 68 5 182 72
- 10. K1={1,4,5} K2={2,3} Individual k1 K2 3 20.80 4.48 4 6.32 14.14 5 2 12.56
- 11. Hierarchical Clustering ď Hierarchical Clustering finds successive clusters using previously established clusters. ď No Assumptions on the number of clusters.
- 12. Agglomerative Hierarchical Clustering ď Initially consider every data point as an individual Cluster and at every step, merge the nearest pairs of the cluster. ďIt is a bottom-up method. ďAt first every data set is considered as individual entity or cluster. ďAt every iteration, the clusters merge with different clusters until one cluster is formed.
- 13. Example of Agglomerative Hierarchical Clustering
- 14. Divisive Hierarchical Clustering ďDivisive Hierarchical clustering is precisely the opposite of the Agglomerative Hierarchical clustering. ďIn Divisive Hierarchical clustering, we take into account all of the data points as a single cluster. ďIn every iteration, we separate the data points from the clusters which arenât comparable. ďIn the end, we are left with N clusters.
- 15. Example of Divisive Hierarchical Clustering
- 16. Reference ⢠https://www.edureka.co/data-science-python-certification-course ⢠https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering- and-different-methods-of- clustering/#:~:text=Clustering%20is%20the%20task%20of,and%20assign%20t hem%20into%20clusters ⢠https://www.google.com/amp/s/www.geeksforgeeks.org/clustering-in-machine- learning/amp/ ⢠https://towardsdatascience.com/k-means-clustering-algorithm-applications- evaluation-methods-and-drawbacks-aa03e644b48a ⢠https://www.kdnuggets.com/2019/09/hierarchical-clustering.html ⢠https://towardsdatascience.com/hierarchical-clustering-agglomerative-and- divisive-explained-342e6b20d710 ⢠https://towardsdatascience.com/understanding-the-concept-of-hierarchical- clustering-technique-c6e8243758ec ⢠https://developers.google.com/machine-learning/clustering/overview ⢠https://www.google.com/amp/s/www.geeksforgeeks.org/hierarchical- clustering-in-data-mining/amp/ ⢠https://www.google.com/amp/s/www.geeksforgeeks.org/k-means-clustering- introduction/amp/
- 17. THANKYOU