Mining%20Data%20Steams%20/%20Incremental%20Data%20Mining%20/%20Mining%20sensor%20data%20(e.g.%20modify%20a%20decision%20tree%20assuming%20that%20new%20examples%20arrive%20continuously,%20and%20old%20examples%20are%20discarded)

1
Other Important Topics in Data Mining that we
didnt or very little discuss in this class

• 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)
• Text Mining
• Mining the Web/Mining Graphs and other complex
  structures
• Mining spatial-temporal data, particularly
  environmental, cell-phone, and traffic data
• Contrast mining (e.g. how do two groups of people
  differ)
• Data Mining and Privacy
• Mining Social Networks (kind of hot these days)
• Statistical Techniques (Principal component
  analysis, multi-dimensional scaling, feature
  selection, statistical testing, Bayesian
  classifier,...)?typically taught in a Machine
  Learning class.
• Preprocessing probably deserves more coverage
• High Performance Data Mining ?Parallel
  Programming Course

2
New Challenges for the Field of Data Mining

• Develop a unifying theory for data mining (e.g.
  explaining how and when over-fitting occurs)
• Mining data streams / mining sensor networks /
  mining sequential data
• High performance data mining platforms /
  combining parallel computing and data mining
  (http//en.wikipedia.org/wiki/Hadoop)
• Spatial data mining / temporal data mining /
  spatial temporal
• Mining graphs and other complex types of data
• More research on the interestingness of knowledge
• Distributed data mining (cannot pass the complete
  data set distributed decision making, e.g. in
  sensor networks)
• Data mining for genomic and earth science
  problems
• What is the data mining process --- kind of
  software engineering for data mining development
  of data mining methodologies
• Data Mining without violating privacy and
  security

3
Complementary Knowledge For Getting Jobs in Data
Mining
Search Techniques
Evolutionary Computing
Information Retrieval
Databases
Software Design
Pattern Recognition
Data Visualization
Data Mining
High Performance Computing
AI
Machine Learning
Image Processing
Data Structures Algorithms
GIS
Experimental Evaluation
Optimization
Statistics
Software Engineering
4
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.