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Perform Clustering on Universities data to make clusters of similar universities

1. Heirarchical Clustering

  • It is a bottom to top approach.

  • Find the euclidian distance between two points. Club closest point in one cluster.

  • Then find the euclidian distance between two cluster / distance between one cluster and one point. We will again club the shortest distance in one cluster.

  • Method to calculate euclidian distance

    • Single Linkage - shortest distance between 2 points in clusters.

    • Average Linkage - largest distance between 2 points in clusters.

    • Complete Linkage - Average of distance between all points in clusters.

    • Centroid Linkage - Find centroids of each clusters and find the distance between their centroids.

  • If the data has both binary and numerical data. First convert the binary categorical data to 0 & 1 and standardize the numerical data. And perform heirarchical clustering on it.

2. K-Means Clustering

  • Mostly used for practical uses. Will be used in industry frequently.

  • Assume K = 3 (# of clusters)

  • Find centroid for each partition

  • Find distance between all data points to all cluster centroids.

  • Move/Reassign datapoints to the nearest centroids.

  • As the data has now changed so Recompute the centroids.

  • Repeat step 3 - 5.

  • Continue till we dont have to move any datapoint closer to any other centroid.

Cluster Profiling - Naming the cluster according to the common characteristics of the points in it.

How to select K-value

  • Plot Elbow graph/ Scree Plot

3. DBSCAN

  • DBSCAN - Density Based Spatial Clustering of Application with Noise.

Disadvantages of K-Means -

  1. Cannot be used on data with noise(outliers).

  2. K-Means does not perform well on non-spherical data. DBSCAN does.

  3. We have to priorly determine the value of k in K-Means.

  • DBSCAN is used for non-spherical data and also where data has lot of outliers, it will identify tehm seperately.

Important Terminology for DBSCAN

  • epsilone - User given parameter - Defines size and borders of each neighbourhood. It will depend on domain and we have to trial and error dependeing how many outliers are we getting.

  • Minpoints - User given parameters - Min number of points in neighnourhood to call it a dense region. Density Threshold. Generally choose minpoints >= D+1. D = no. of columns.

  • Core point -

  • Border point

  • Noisy point

Disadvantage of DBSCAN

  • Difficult with data whose density is low.

  • It is an itterative procedure to determine eps and minpoints.

  • computational complexity.