Hierarchical Clustering in Data Mining

Hierarchical Clustering in Data Mining

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  School of DataScience and Forecasting MBA -Business Analytics Presentation of Data Mining and Warehousing On Hierarchical Clustering Technique Presented By: Yashraj Nigam Tanvi Bhave Anjali Agarwal Presented To: Mr. Viney Sharma
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  CLUSTERING Clustering is thetask of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. Let’s understand this with an example. Suppose, you are the head of a rental store and wish to understand preferences of your customers to scale up your business. Is it possible for you to look at details of each customer and devise a unique business strategy for each one of them? Definitely not. But, what you can do is to cluster all of your customers into say 10 groups based on their purchasing habits and use a separate strategy for customers in each of these 10 groups. And this is what we call clustering.
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  CLUSTERING APPLICATIONS Clustering algorithmscan be applied in many fields, for instance: a) Marketing: finding groups of customers with similar behavior given a large database of customer data containing their properties and past buying records b) Biology: classification of plants and animals given their features c) Libraries: book ordering d) Insurance: identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds e) City-planning: identifying groups of houses according to their house type, value and geographical location f) Earthquake studies: clustering observed earthquake epicenters to identify dangerous zones
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  Types of AgglomerativeTechniques • Single-linkage Technique • Complete-linkage Technique • Average linkage Technique
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  Single-Linkage Technique Minimum distanceclustering is also called as single linkage hierarchical clustering or nearest neighbor clustering. Distance between two clusters is defined by the minimum distance between objects of the two clusters, as shown below.
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  Complete-Linkage Technique A connectedcomponent is a maximal set of connected points such that there is a path connecting each pair. A clique is a set of points that are completely linked with each other.
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  Pictorial Analysis
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  Implementing Hierarchical Clustering on WEKA
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  1. SELECT THEDATASET FOR CLUSTERING
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  2. CLICK ONCLUSTER TAB AND CHOOSE HIERARICHAL CLUSTERER
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  3. DOUBLE CLICKON HIERARCHICAL CLUSTERER TO CHANGE NUMBER OF CLUSTERS AND DISTANCE FUNCTION
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  4. CLICK ONSTART TO INITIATING CLUSTERING PROCESS
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  5. RIGHT CLICKON RESULT AND SELECT VISUALIZE CLUSTER ASSIGNMENT
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  6. INTERPRET THERESULTS