Data Mining II - Advanced Topics in Data Mining

 

Information about the inspection of the exam paper is available here.

Timetable

DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Mon. 15:00 bis 17:00 weekly G22A-210 (24 Plätze) Spiliopoulou Lehrpreisträger/-in   20
Übung (Ü) - Exercise - Dates/Times/Location:
Thu.09:00 bis 11:00weeklyG22A-210 (24 Plätze) Tutor  20

Overview (from LSF)

Learning Content

In this course, we discuss advanced Data Mining methods for Data Science:

* Dealing with VELOCITY: methods for supervised, semi-supervised and unsupervised learning on data streams

* Dealing with VOLATILITY: learning and adaption on dynamic data

* Dealing with VOLUME: methods for learning on high-dimensional data

* VERACITY: incorporating expert knowledge into the learning process

From the applications' perspective, we focus on web applications and on applications from the domain of medical research.

Literature

Scientific papers (to be announced at the course)

Prerequisites

Data Mining (recommended)

Target Group

WPF Master DKE

WPF Master Inf

WPF Master WIF

WPF Master CV

WPF Master IngInf

WPF Master Statistik

Description Data Mining II - Advanced Topics in Data Mining

Lecture

Block 0 - Introduction

Block 1a - Mining Volatile Data (UPDATE 15.05.2017)

Block 1b - Mining Volatile Data

Block 1c - Mining Volatile Data

Block 1c - Part 2 (UPDATE: 19.06.2017)

Exercise

Exercises - General Information

Exercise 1

Exercise 2 (Update 15.05.2017)

Exercise 3

Exercise 4

Exercise 4 - Slides

Exercise 5

Programming Assignment

CluStream

Last Modification: 17.10.2017 - Contact Person:

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