Conceptual Clustering and its Application to Concept Drift and Novelty Detection

author: Claudia d'Amato, University of Bari

You might be experiencing some problems with Your Video player.

*Slides*
0:00	Conceptual Clustering: Concept Formation, Drift and Novelty Detection
0:11	Contents
0:40	Introduction & Motivation
1:35	Basics on Clustering Methods
2:06	Basics on Clustering Methods
2:18	Basics on Clustering Methods
2:34	Conceptual Clustering: Related Works
3:40	Reference Representation
3:57	Semi-Distance Measure: Main Idea
4:13	Semantic Semi-Dinstance Measure: Definition
4:38	Semi-Distance Measure: Discussion
5:24	Semi-Distance Measure: Discussion
5:50	Clustering Algorithm: Characteristics
6:43	Running the Clustering Algorithm
8:45	Clustering Algorithm: Main Idea
10:07	Conceptual Clustering Step
11:53	Automated Concept Drift and Novelty Detection
12:50	Evaluating the Candidate Cluster: Main Idea 1/2
13:30	Evaluating the Candidate Cluster: Main Idea 2/2
14:10	Evaluating the Candidate Cluster
15:08	Evaluating the Candidate Cluster: Main Idea 1/2
15:14	Evaluating the Candidate Cluster
15:32	Evaluating Concept Drift and Novelty Detection
16:17	Evaluating the Candidate Cluster: Main Idea 2/2
16:31	Evaluating Concept Drift and Novelty Detection
16:47	Experimental Setting
17:36	Evaluation Methodology
17:59	Experimental Results 1/3
19:05	Experimental Results 2/3
19:11	Experimental Results 3/3
19:44	Conclusions
20:05	Future Works
21:34	The End
21:45	- Questions

Lecture rating

People found this lecture:
Worth seeing
because it is:
Valuable and informative
Well presented
Easily understandable
Acceptably recorded
You need to login to cast your vote.

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.