Cluster Analysis in Data Mining

However, the topic of evaluation is very dense in the lectures and the provided book chapters do not provide relevant insights as well, making the programming assignment for this part quite challenging (at least if not already deeply familiar already with the concepts involved).

Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.

Cluster Analysis in Data Mining is third course in Coursera's new data mining specialization offered by the University of Illinois Urbana-Champaign.

Cluster Analysis in Data Mining is third course in Coursera's new data mining specialization offered by the University of Illinois Urbana-Champaign.

Now I don't mind briefly covering topics, understanding that cluster analysis is a complex topic with many facets.

Cluster Analysis is taught by Professor Jiawei Han who was the instructor for the first course in the data mining specialization: Pattern Discovery in Data Mining.

An optional programming assignment was added half way through the course; in a course about data mining, programming assignments should be front and center, not added as an afterthought to quell an outcry from students.

However, it was a fun experience, but I hope in the second iteration that the ratio of the programming assignments and the theoretical descriptions of various algorithms and papers will be equal.

The course is a 4-week overview of data clustering: unsupervised learning methods that attempt to group data into clusters of related or similar observations.

The reason I chose to run unsupervised clustering on this BOSCH dataset, which is ostensibly intended for supervised learning, is to eliminate significant amounts of the missing data from being exposed to multiple individual supervised learning models by prior clever grouping of examples.

I found the lecture material unclear or vague at times, so that for certain topics understanding heavily depends on one diving through the provided reading material (which I found very helpful).

The course is very insightful and very helpful for the data mining studies at university courses.

The course covers two most common clustering methods--K means and hierarchical clustering--as well as more than a dozen other clustering algorithms.

This course along with the Reading material proposed will give you a big picture of how clustering algorithms work, as well as clustering validation methodologies.

It has about 90% missing data in every row, and there are 2 million rows in total, and about 4500 columns!