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Geographic Data Mining Marc van Kreveld Seminar for GIVE Block 1, 2003/2004
About … <ul><li>A form of geographical analysis </li></ul><ul><li>Current topic of interest in GIS research (and database ...
This seminar <ul><li>Learning about a topic together </li></ul><ul><li>Presenting to each other + interaction </li></ul><u...
Material <ul><li>Book by Harvey Miller and Jiawei Han (editors): selected chapters </li></ul><ul><li>Possibly: papers from...
Weeks <ul><li>Week 36-46 </li></ul><ul><li>Probably: </li></ul><ul><ul><li>Not September 4 (this Thursday) </li></ul></ul>...
Overview of Geographic Data Mining & Knowledge Discovery <ul><li>Chapter 1 of the book </li></ul><ul><li>KDD: knowledge di...
Knowledge Discovery in Databases (KDD) <ul><li>Large databases contain interesting  patterns : non-random properties and r...
Knowledge Discovery in Databases (KDD) <ul><li>Because of quantity of data nowadays </li></ul><ul><li>Because we want info...
KDD opposed to statistics <ul><li>Statistics </li></ul><ul><ul><li>small and clean numeric database </li></ul></ul><ul><ul...
KDD techniques <ul><li>Statistics </li></ul><ul><li>Machine learning </li></ul><ul><li>Pattern recognition </li></ul><ul><...
Data warehouse <ul><li>Large repository of data </li></ul><ul><li>F or analytical processing  (DB: transactional processin...
OLAP Example <ul><li>M easure of interest: sales </li></ul><ul><li>D imensions of interest: item, store, week </li></ul><u...
OLAP Example <ul><li>2-dim. aggregation: (item, store, . )    money </li></ul><ul><li>A nother 2-dim. aggregation: sales ...
KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li...
KDD steps <ul><li>Data selection : which records, variables chosen? </li></ul>
KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing : removing noise, duplicate records, handling missi...
KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment : combining the s...
KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li...
KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li...
KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li...
Data mining <ul><li>Segmentation </li></ul><ul><li>Dependency analysis </li></ul><ul><li>Deviation and outlier analysis </...
DM - segmentation <ul><li>Description: </li></ul><ul><li>Clustering : finding a finite set of implicit classes </li></ul><...
DM - segmentation clustering given classes classification
DM – dependency analysis <ul><li>Description: </li></ul><ul><li>Finding rules to predict the value of some attribute based...
DM – dependency analysis <ul><li>Confidence  and  support  measures for association rules of the form: [  if  X  then  Y ]...
DM – deviation & outlier analysis <ul><li>Description: </li></ul><ul><li>Finding data with unusual deviations (=errors, or...
DM – trend detection <ul><li>Description: </li></ul><ul><li>Finding lines, curves, summarizing the database (often as a fu...
DM – generalization and characterization <ul><li>Description: </li></ul><ul><li>Obtaining compact descriptions of the data...
Visualization and knowledge discovery <ul><li>KDD is difficult to automate    steered by human intelligence </li></ul><ul...
KD + geography <ul><li>Special case of KDD </li></ul><ul><li>Other special cases </li></ul><ul><ul><li>marketing </li></ul...
KD + geography (attr1, attr2, attr3, attr4); attr’s are numbers and (relatively) independent: statistics (attr1, attr2, at...
KD + geography <ul><li>Study of scalable versions of DM tasks (in lat. and long.) </li></ul><ul><li>Certain dimensions can...
Geographic data mining <ul><li>Spatial segmentation (clustering, classification) </li></ul><ul><li>Spatial dependency (spa...
GDM – spatial association rules <ul><li>Example:  If  a location is within 500 m from water and the average winter tempera...
GDM – spatial trend detection <ul><li>Patterns of change with respect to neighborhood of some object </li></ul><ul><li>Exa...
GDM - applications <ul><li>Map interpretation </li></ul><ul><li>Remote sensing interpretation </li></ul><ul><li>Environmen...
Conclusions <ul><li>GDM & GKD is an extension of (tool for) geographical analysis </li></ul><ul><li>GDM is different from ...
This seminar on GDM <ul><li>First: chapters from the book </li></ul><ul><ul><li>CH 1: GDM & KD: an overview  (today) </li>...
This seminar <ul><li>All PowerPoint presentations on the Web page of the course </li></ul><ul><li>Survey paper or written ...
Each presentation <ul><li>The chapter contents </li></ul><ul><li>Additional (spatial) examples (from the Web links or self...
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Geographic Data Mining

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Geographic Data Mining

  1. 1. Geographic Data Mining Marc van Kreveld Seminar for GIVE Block 1, 2003/2004
  2. 2. About … <ul><li>A form of geographical analysis </li></ul><ul><li>Current topic of interest in GIS research (and database research and AI research) </li></ul><ul><li>Finding hidden information in large collections of geographic data </li></ul>
  3. 3. This seminar <ul><li>Learning about a topic together </li></ul><ul><li>Presenting to each other + interaction </li></ul><ul><li>Added value by good examples: </li></ul><ul><ul><li>for important concepts, algorithms </li></ul></ul><ul><ul><li>possibly self-thought of, or extended </li></ul></ul><ul><ul><li>referring to GIS data and issues (hence the GIS course prerequisite) </li></ul></ul><ul><li>Written assignment: joint survey </li></ul>
  4. 4. Material <ul><li>Book by Harvey Miller and Jiawei Han (editors): selected chapters </li></ul><ul><li>Possibly: papers from conference proceedings </li></ul><ul><li>Mostly provided by me </li></ul>
  5. 5. Weeks <ul><li>Week 36-46 </li></ul><ul><li>Probably: </li></ul><ul><ul><li>Not September 4 (this Thursday) </li></ul></ul><ul><ul><li>Not in week 40 (Sept. 29 & Oct. 2) </li></ul></ul><ul><ul><li>Not October 23 </li></ul></ul><ul><li>The above depending on participation! </li></ul>
  6. 6. Overview of Geographic Data Mining & Knowledge Discovery <ul><li>Chapter 1 of the book </li></ul><ul><li>KDD: knowledge discovery in databases </li></ul><ul><li>Data warehouses </li></ul><ul><li>Data mining </li></ul><ul><li>Geographic aspects of the above </li></ul>
  7. 7. Knowledge Discovery in Databases (KDD) <ul><li>Large databases contain interesting patterns : non-random properties and relationships that are: </li></ul><ul><ul><li>valid (general enough to apply to new data) </li></ul></ul><ul><ul><li>novel (non-trivial and unexpected) </li></ul></ul><ul><ul><li>useful (leads to effective action: decision making or investigation) </li></ul></ul><ul><ul><li>ultimately understandable (simple, and interpretable by humans) </li></ul></ul>
  8. 8. Knowledge Discovery in Databases (KDD) <ul><li>Because of quantity of data nowadays </li></ul><ul><li>Because we want information, not data </li></ul><ul><li>Because computing power allows it </li></ul>
  9. 9. KDD opposed to statistics <ul><li>Statistics </li></ul><ul><ul><li>small and clean numeric database </li></ul></ul><ul><ul><li>scientifically sampled </li></ul></ul><ul><ul><li>specific questions in mind </li></ul></ul><ul><li>KDD: none of the above </li></ul>
  10. 10. KDD techniques <ul><li>Statistics </li></ul><ul><li>Machine learning </li></ul><ul><li>Pattern recognition </li></ul><ul><li>Numeric search (?) </li></ul><ul><li>Scientific visualization </li></ul>
  11. 11. Data warehouse <ul><li>Large repository of data </li></ul><ul><li>F or analytical processing (DB: transactional processing) </li></ul><ul><li>H eterogeneous : different sources and formats (DB: homogeneous) </li></ul><ul><li>S upports OLAP tools (OnLine Analytical Processing) </li></ul>
  12. 12. OLAP Example <ul><li>M easure of interest: sales </li></ul><ul><li>D imensions of interest: item, store, week </li></ul><ul><li>(item, store, week)  money [quantity sold times price ] </li></ul>
  13. 13. OLAP Example <ul><li>2-dim. aggregation: (item, store, . )  money </li></ul><ul><li>A nother 2-dim. aggregation: sales by store and by week </li></ul><ul><li>1-dim. a gg regatio n : sales by week (all items and stores) </li></ul><ul><li>Data cube : all 2 d possible aggregations, different types of summaries </li></ul>
  14. 14. KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li>Data reduction and projection </li></ul><ul><li>Data mining </li></ul><ul><li>Interpretation and reporting </li></ul>Presence of steps and order not fixed
  15. 15. KDD steps <ul><li>Data selection : which records, variables chosen? </li></ul>
  16. 16. KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing : removing noise, duplicate records, handling missing data, … </li></ul>
  17. 17. KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment : combining the selected data with external data </li></ul>
  18. 18. KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li>Data reduction and projection : reduction in number, reducing dimension </li></ul>
  19. 19. KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li>Data reduction and projection </li></ul><ul><li>Data mining : uncovering information, interesting patterns </li></ul>
  20. 20. KDD steps <ul><li>Data selection </li></ul><ul><li>Data pre-processing </li></ul><ul><li>Data enrichment </li></ul><ul><li>Data reduction and projection </li></ul><ul><li>Data mining </li></ul><ul><li>Interpretation and reporting : evaluating, understanding, communicating </li></ul>
  21. 21. Data mining <ul><li>Segmentation </li></ul><ul><li>Dependency analysis </li></ul><ul><li>Deviation and outlier analysis </li></ul><ul><li>Trend detection </li></ul><ul><li>Generalization and characterization </li></ul>
  22. 22. DM - segmentation <ul><li>Description: </li></ul><ul><li>Clustering : finding a finite set of implicit classes </li></ul><ul><li>Classification : mapping data items into pre-defined classes </li></ul><ul><li>Techniques: </li></ul><ul><li>Cluster analysis </li></ul><ul><li>Bayesian classification </li></ul><ul><li>Decision or classification trees </li></ul><ul><li>Artificial neural networks </li></ul>
  23. 23. DM - segmentation clustering given classes classification
  24. 24. DM – dependency analysis <ul><li>Description: </li></ul><ul><li>Finding rules to predict the value of some attribute based on other attributes </li></ul><ul><li>Techniques: </li></ul><ul><li>Bayesian networks </li></ul><ul><li>Association rules </li></ul>(4, 12, 0.24) (3, 14, 0.21) (7, 13, 0.43) (2, 9, 0.78) (11, 11, 0.55) (5, 11, ???) (???, 12, 0.51)
  25. 25. DM – dependency analysis <ul><li>Confidence and support measures for association rules of the form: [ if X then Y ]: confidence = #(X and Y in DB) / #(X in DB) support = #(X and Y in DB) / #(all in DB) </li></ul>
  26. 26. DM – deviation & outlier analysis <ul><li>Description: </li></ul><ul><li>Finding data with unusual deviations (=errors, or data of particular interest) </li></ul><ul><li>Techniques: </li></ul><ul><li>Clustering, other mining methods </li></ul><ul><li>Outlier analysis </li></ul>
  27. 27. DM – trend detection <ul><li>Description: </li></ul><ul><li>Finding lines, curves, summarizing the database (often as a function over time) </li></ul><ul><li>Techniques: </li></ul><ul><li>Regression </li></ul><ul><li>Sequential pattern extraction </li></ul>
  28. 28. DM – generalization and characterization <ul><li>Description: </li></ul><ul><li>Obtaining compact descriptions of the data </li></ul><ul><li>Techniques: </li></ul><ul><li>Summary rules </li></ul><ul><li>Attribute-oriented induction </li></ul>concept hierarchy low level concept higher level concept
  29. 29. Visualization and knowledge discovery <ul><li>KDD is difficult to automate  steered by human intelligence </li></ul><ul><li>Visualization helps to understand the data and which data mining techniques to try </li></ul>
  30. 30. KD + geography <ul><li>Special case of KDD </li></ul><ul><li>Other special cases </li></ul><ul><ul><li>marketing </li></ul></ul><ul><ul><li>biology </li></ul></ul><ul><ul><li>astronomy </li></ul></ul><ul><li>Main features: location, distance, dimen-sionality (with dependent dimensions) </li></ul>
  31. 31. KD + geography (attr1, attr2, attr3, attr4); attr’s are numbers and (relatively) independent: statistics (attr1, attr2, attr3, attr4); attr’s can also be on other measurement scales: KDD (attr1, attr2, attr3, attr4); attr’s are often dependent and can be shapes: KD + geography Often: (lat., long., attr1, attr2, …) or: (shape description, attr1, attr2, …)
  32. 32. KD + geography <ul><li>Study of scalable versions of DM tasks (in lat. and long.) </li></ul><ul><li>Certain dimensions can be non-metric (travel time need not be symmetric) </li></ul><ul><li>DM in data that is not in the form of tuples: sets of thematic map layers </li></ul>
  33. 33. Geographic data mining <ul><li>Spatial segmentation (clustering, classification) </li></ul><ul><li>Spatial dependency (spatial association rules) </li></ul><ul><li>Spatial trend detection </li></ul><ul><li>Geographic characterization and generalization </li></ul>
  34. 34. GDM – spatial association rules <ul><li>Example: If a location is within 500 m from water and the average winter temperature is at least –2 degrees then there are frogs around </li></ul>distance relationship
  35. 35. GDM – spatial trend detection <ul><li>Patterns of change with respect to neighborhood of some object </li></ul><ul><li>Example: (North America) Further from Pacific ocean  fewer earthquakes </li></ul>
  36. 36. GDM - applications <ul><li>Map interpretation </li></ul><ul><li>Remote sensing interpretation </li></ul><ul><li>Environmental mapping (soil type, etc.) </li></ul><ul><li>Extracting spatio-temporal patterns for cyclones, crimes </li></ul><ul><li>Spatial interaction (movement/flow of people, capital, goods) </li></ul>
  37. 37. Conclusions <ul><li>GDM & GKD is an extension of (tool for) geographical analysis </li></ul><ul><li>GDM is different from DM due to </li></ul><ul><ul><li>Geographic spaces, not attribute space </li></ul></ul><ul><ul><li>Neighborhood is extremely important </li></ul></ul><ul><ul><li>Scale issues </li></ul></ul><ul><ul><li>Data is different </li></ul></ul><ul><ul><li>Applications (interesting patterns to mine for) are different </li></ul></ul>
  38. 38. This seminar on GDM <ul><li>First: chapters from the book </li></ul><ul><ul><li>CH 1: GDM & KD: an overview (today) </li></ul></ul><ul><ul><li>CH 2: Paradigms for spatial and spatio-temporal DM(11-9) </li></ul></ul><ul><ul><li>CH 3: Fundamentals of spatial DW for GKD (15-9) </li></ul></ul><ul><ul><li>CH 7: Algorithms and applications of SDM (Ronny) (18-9) </li></ul></ul><ul><ul><li>CH 8: Spatial clustering in DM (22-9) </li></ul></ul><ul><ul><li>CH 6: Modeling spatial dependencies (25-9) (not: 29-9 and 2-10) </li></ul></ul><ul><ul><li>CH 9: Detecting outliers (6-10) </li></ul></ul><ul><ul><li>CH 10: Knowledge construction based on GVis and KDD </li></ul></ul><ul><ul><li>CH 14: Mining mobile trajectories </li></ul></ul>
  39. 39. This seminar <ul><li>All PowerPoint presentations on the Web page of the course </li></ul><ul><li>Survey paper or written exam; possible topics for survey: </li></ul><ul><ul><li>Hierarchical clustering </li></ul></ul><ul><ul><li>Clustering with obstacles </li></ul></ul><ul><ul><li>Proximity relationship mining </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Or: joint survey of (geometric) algorithms for GDM </li></ul>
  40. 40. Each presentation <ul><li>The chapter contents </li></ul><ul><li>Additional (spatial) examples (from the Web links or self-constructed) </li></ul><ul><li>Detect and present algorithmic problems that appear  together: report on algorithmic issues in GDM </li></ul><ul><li>Present your chapter; don’t be afraid of overlap with other chapters </li></ul>

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