Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and
forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called
Knowledge Discovery that deals with extracting useful knowledge from raw data.
The knowledge discovery process includes data selection, cleaning, coding, using
different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole
process by examples. Special emphasis will be give to the Machine Learning methods as they provide the real knowledge discovery tools. Important related
technologies, as data warehousing and on-line analytical processing (OLAP) will be also discussed. The students will use recent Data Mining software.
Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data and tidy the data using a software program. The steps that convert data from its raw form to the tidy form is called data wrangling.
This process is a critical step for any data mining person or scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.
| Required reading: | Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005, ISBN: 0-12-088407-0. |
| Weka 3 : | Data Mining System with Free Open Source Machine Learning Software in Java. Available at http://www.cs.waikato.ac.nz/~ml/weka/index.html |
Curriculum
- 12 Sections
- 65 Lessons
- 10 Weeks
- Section 9: Evaluating what's been learned6
- 1.1Lesson : Basic issues
- 1.2Lesson : Training and testing
- 1.3Lesson : Estimating classifier accuracy (holdout, cross-validation, leave-one-out)
- 1.4Lesson : Combining multiple models (bagging, boosting, stacking)
- 1.5Lesson : Minimum Description Length Principle (MLD)
- 1.6Lesson : Experiments with Weka – training and testing
- Section 10 : Mining real data2
- Section 11: Clustering7
- 3.1Lesson : Basic issues in clustering
- 3.2Lesson : First conceptual clustering system: Cluster/2
- 3.3Lesson : Partitioning methods: k-means, expectation maximization (EM)
- 3.4Lesson : Hierarchical methods: distance-based agglomerative and divisible clustering
- 3.5Lesson : Conceptual clustering: Cobweb
- 3.6Lesson : Experiments with Weka – k-means, EM, Cobweb
- 3.7Lesson : Mining specific data types such as time-series, social networks, multimedia, and Web data
- Section 12: Advanced techniques, Data Mining software and applications6
- 4.1Lesson : Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
- 4.2Lesson : Bayesian approach to classifying text
- 4.3Lesson : Web mining: classifying web pages, extracting knowledge from the web
- 4.4Lesson : Data Mining software and applications
- 4.5three quizes10 Minutes0 Questions
- 4.6Lesson : Mining specific data types such as time-series, social networks, multimedia, and Web data
- Section 1: Introduction to Data MiningAppellat his assignatum kakan licet bene ergo placet iustam solet physicum constituta prope polliceretur immo8
- 5.1Lesson 1: What is data mining?
- 5.2Lesson 2: Related technologies – Machine Learning, DBMS, OLAP, Statistics
- 5.3Lesson 3: Data Mining Goals
- 5.4Lesson 4: Stages of the Data Mining Process
- 5.5Lesson 5: Data Mining Techniques
- 5.6Lesson 6: Knowledge Representation Methods
- 5.7Lesson 7: Applications
- 5.8Quiz 1Copy10 Minutes13 Questions
- Section 2 : Data Warehouse and OLAPIlla utilitates superabat libentius mortuum aliqua ultimum consequentia magnam consentaneum pueri5
- Section 3 : Data preprocessingContemnere convenit oritur d dissimilis quoquo cognitioque cariorem dixisset videremus officia tributa ducitur7
- Section 4 : Data mining knowledge representationQuaerenda delectabatur verbi idemne ducem captum caret meliusque utram existimas facilius sane lustravit pericli7
- Section 5 : Attribute-oriented analysisAcies levitatis relinquo sapientia finxerit debeas sapienter vivatur istius vitio ordiamur epuletur6
- Section 6 : Data mining algorithms: Association rulesRatio turpitudinis vitae reperire praeceptum pertectam aristidem arte quoniam declaret sextus cui7
- 10.1Lesson 60: Motivation and terminology
- 10.2Lesson 61: Example: mining weather data
- 10.3Lesson 62: Basic idea: item sets
- 10.4Lesson 63: Generating item sets and rules efficiently
- 10.5Lesson 64: Correlation analysis
- 10.6Lesson 65:Experiments with Weka – mining association rules
- 10.7Quiz 6Copy20 Minutes13 Questions
- Section 7 : Data mining algorithms: ClassificationDasne paulumque sine auditor ceteris bonis consequens attinet iustus ortus reperiemus sempiternam6
- Section 8: Data mining algorithms: Prediction6
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