Data Warehousing and Mining – K-Mean Clustering

K-Mean Clustering is a method of vector quantization signal processing which is popular for cluster analysis. It is a data analysis technique, groups object together with non-hierarchical method, determines the centroid using Euclidean method, and groups objects based on minimum distance. K-Mean Clustering is a part of Data Warehousing and Mining used to determine distances between clusters. This course helps you to mathematically determine the distances between clusters, that is applying K-Mean Clustering.

Hierarchical Clustering in data mining and statistics is a method of cluster analysis which seeks to build a hierarchy of clusters. HAC has three main concepts Single-nearest distance or single linkage, Complete-farthest distance or complete linkage, and Average-average distance or average linkage.

• Single Linkage is the distance between the closest members of the two clusters
• Complete Linkage is the distance between the farthest members of the two clusters
• Average Linkage involves looking at the distance between all pairs and averages of all these distances.
• Finding a cluster on the given example applying Single Link, Complete Link, and Average Link techniques.
• Understand the steps required to perform K-Mean Clustering on a single Data set.
• Apriori Algorithm and its implementation in data warehousing and mining
• Use Apriori Algorithm to mine frequent itemsets for Boolean association rules using bottom-up approach
• Apply Naïve Bayes Classifier for Data Warehouse and Data Mining
• These classifiers predict class membership probabilities and use prior probability of each category
• Apply the classifier in an example and derive mathematical solution for the cluster classifier