Of k-Means Clustering
Today we will talk about k-Means clustering …. What it is and where did the term k-means come from.
As a student of marketing we will also try to understand a little about how can k-Means clustering be used in marketing.
The term "k-means" was first used by James MacQueen in 1967, though the idea goes back to Hugo Steinhaus in 1957. The standard algorithm was first proposed by Stuart Lloyd in 1957 as a technique for pulse-code modulation, though it wasn't published until 1982.
In statistics and data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. The main advantages of this is that it is simple and the most popular method of partitioning data.
To start with today was my second class in Business Analytics on SPSS….. not to mention how I dreaded this class even before it started due to my weakness with numbers. I did never realize that SPSS could so simple a tool to handle even though it is loaded with various resources to make analysis and one of them is what we I am going to talk about.
The benefits of k-Means clustering to a marketing student can be in various fields:
Retail: Can be used to cluster similar Merchandise.
Market Research: Can be used to cluster similar data like demography, etc
For Academic Purpose:
The k-Means Clustering algorithm can be used for prediction of students academic performance.
At the end I would like to say that k-means is a very convenient way to cluster large number of data and also helpful to a marketer.
Author: Archit Tamakhuwala
Group Name: Marketing 2