last words dm 1. mining data steams / incremental data mining / mining sensor data (e.g. modify a...
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Last Words DM
1. Mining Data Steams / Incremental Data Mining / Mining sensor data (e.g. modify a decision tree assuming that new examples arrive continuously, and old examples are discarded)
2. Text Mining 3. Mining the Web/Mining Graphs and other complex structures 4. Mining spatial-temporal data, particularly environmental, cell-phone,
and traffic data 5. Contrast mining (e.g. how do two groups of people differ)6. Data Mining and Privacy 7. Mining Social Networks (kind of hot these days) 8. Statistical Techniques (Principal component analysis, multi-
dimensional scaling, feature selection, statistical testing, Bayesian classifier,...)typically taught in a Machine Learning class.
9. Preprocessing probably deserves more coverage10. High Performance Data Mining Parallel Programming Course
Other Important Topics in Data Mining that we didn’t or very little discuss in this class
Other Important Topics in Data Mining that we didn’t or very little discuss in this class
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Last Words DM
1. Develop a unifying theory for data mining (e.g. explaining how and when over-fitting occurs)
2. Mining data streams / mining sensor networks / mining sequential data3. High performance data mining platforms / combining parallel
computing and data mining (http://en.wikipedia.org/wiki/Hadoop) 4. Spatial data mining / temporal data mining / spatial temporal 5. Mining graphs and other complex types of data6. More research on the interestingness of knowledge7. Distributed data mining (cannot pass the complete data set; distributed
decision making, e.g. in sensor networks)8. Data mining for genomic and earth science problems9. What is the data mining process --- kind of software engineering for
data mining; development of data mining methodologies…10. Data Mining without violating privacy and security
New Challenges for the Field of Data MiningNew Challenges for the Field of Data Mining
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Last Words DM
Complementary Knowledge For Getting Jobs in Data Mining
Data Mining
Databases
Data Structures
& Algorithms
Software Design
Machine Learning
AI
High Performance
Computing
Evolutionary
Computing
Pattern Recognition
Statistics
Optimization
Information Retrieval
Image Processing
GIS
Data Visualization
Search Techniques
Experimental
Evaluation
Software Engineering
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Last Words DM
2008 Student Textbook Evaluation Overall positive evaluation but
– Some felt that algorithms were not explained in sufficient detail, particularly examples are missing
– A few felt the material should be better indexed
– Some felt it lack highlighting of key points
– Some felt it is at an intermediate level, and does not give sufficient depth if the textbook is your only source of knowledge; it also introduces topics more intuitively and not formally, as some more advanced textbook do.
2 students felt that the textbook does not introduce topics very clearly, and that it is not comprehensive.