Clustering in Data Science - Search localsearch

mit.edu

https://www.mit.edu/~georg/papers/lecture6.ppt

Recall that the goal is to group together “similar” data – but what does this mean? No single answer – it depends on what we want to find or emphasize in the data; this is one reason why clustering is an “art” The similarity measure is often more important than the clustering algorithm used – don’t overlook this choice!

ucdavis.edu

https://web.cs.ucdavis.edu/~koehl/Teaching/BL5229_…

Clustering - UC Davis

Once a Clustering has been obtained, it is important to assess its validity! The questions to answer: Did we choose the right number of clusters? Are the clusters compact? Are the clusters well separated?

neu.edu

https://course.ccs.neu.edu/cs6140sp15/7_locality_c…

Introduction to Clustering - Northeastern University

Gain insight into the structure of the data. Find prototypes in the data. Until now we have assumed that the training data is labeled. Now we look at what can we do with data when we have not labels. \item Example: group customer behavior, find similar buying patterns. Goal of Clustering.

rug.nl

https://www.ai.rug.nl/ki2/slides/ki2-s07-clusterin…

Clustering Algorithms - University of Groningen

the goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? It can be shown that there is no absolute “best” criterion which would be independent of the final aim of the clustering.

uh.edu

https://www.bauer.uh.edu/nbsyam/documents/MktRes-M…

Slide 1

-Divisive clustering starts with all the objects grouped in a single cluster. Clusters are divided or split until each object is in a separate cluster. Agglomerative methods are commonly used in marketing research. They consist of linkage methods, variance methods, and centroid methods.

ucr.edu

https://www.cs.ucr.edu/~eamonn/teaching/cs235/clus…

PowerPoint Presentation

In general, this is a unsolved problem. However there are many approximate methods. In the next few slides we will see an example. For our example, we will use the familiar katydid/grasshopper dataset. However, in this case we are imagining that we do NOT know the class labels. We are only clustering on the X and Y axis values.

odu.edu

https://www.cs.odu.edu/~sampath/courses/f19/cs620/…

Lecture 1- Introduction

However, in practice, it’s a bit less clear: there are many ways of influencing the outcome of clustering: number of clusters, similarity measure, representation of documents.