Data Mining - Clustering and Association - Badge
Name
Data Mining - Clustering and Association.
Issuer
Università degli Studi di Milano-Bicocca.
Issued since 15 July 2016.
Description
This Badge is earned by learners participating in the course "Data Mining - Clustering and Association" offered by EduOpen. The Badge is to all intents and purposes the course’s certificate of attendance.
Badge Criteria
This BADGE has been issued to the student who attendend, on the EduOpen Mooc Platform, the course on "Data Mining: Clustering and Association" teached by Prof. Fabio Stella of the Department of Informatics, Systems and Communication of the University of Milano-Bicocca. The student watched methodology and hands-on video lectures about the following topics; how to measure the proximity between attributes of different types, similarity and distance. The student also watched methodology and hands-on video lectures about; formulation and solution of clustering problems when using different types of attributes, how to apply partitioning, hierarchical, density based, and graph based clustering methods. The student was watched methodology and hands-on video lectures on how to validate a clustering solution and how to select the “optimal number of clusters (whatever it means). Furthermore, the student watched methodology and hands-on video lectures explaining how to extract association rules from transaction data an how to sort them according to different relevance measures. The student used the KNIME open source software platform to perform practice sessions, and he/she developed and to uploaded, to the EduOpen Mooc plarform, 10 KNIME workflows. ",
Skills
The owner of this BADGE has the following competences: How to measure similarity/distance between two records. How to formulate a clustering problem. How to develop clustering models according to different paradigms, partitioning, hierarchical, density and graph based. How to validate a clustering model, how many clusters to use. Find which extracted rules are relevant/interesting. How to develop a KNIME workflow to formulate and solve a clustering problem. How to develop a KNIME workflow to formulate and solve a rule extraction problem.
Tags
ComputerandDataSciences, Classification, Datamining