Download Database Clustering and Summary Generation

Other Important Topics in Data Mining
that we didn’t or very little discuss in this class
Big Data (frameworks and environments to analyze big datasets) has
become a hot topic; it is a mixture of data analysis, data mining, high
performance computing, and data bases)
2. Mining Social Networks (kind of hot these days)
3. 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)
4. Text Mining
5. Mining the Web/Mining Graphs and other complex structures
6. Mining spatial-temporal data, particularly environmental, cell-phone,
and traffic data
7. Contrast mining (e.g. how do two groups of people differ)
8. Data Mining and Privacy
9. Statistical Techniques (Principal component analysis, multidimensional scaling, feature selection, statistical testing, Bayesian
classifier,...)typically taught in a Machine Learning class. Last Words DM
1.
New Challenges for the Field of Data Mining
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
data
3. 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 data
6. More research on the interestingness of knowledge
7. Distributed and parallel data mining (cannot pass the complete data
set; distributed decision making, e.g. in sensor networks)
8. Data mining for genomic and earth science problems
9. 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
1.
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Complementary Knowledge
For Getting Jobs in Data Mining
Search Techniques
Information Retrieval
Software Design
Data Visualization
Databases
Pattern Recognition
Data Mining
AI
Image Processing
Evolutionary
Computing
High Performance
Computing
Machine Learning
GIS
Optimization
Data Structures
Experimental & Algorithms
Evaluation
Statistics
Software Engineering
Trend: Data Analytics has become quite hot these daysmore jobs
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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.
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