Spatial Data Mining and Spatial Data Warehousing Special Topics In Database

1
Spatial Data Mining and Spatial Data
WarehousingSpecial Topics In Database

• Sadra Abedinzadeh
• Ashkan Zarnani
• Farzad Peyravi

2
Outline

• Motivation and General Description
• Data Warehousing Basic Concepts and Techniques
  
• Spatial Data Warehousing and Spatial OLAP
  Techniques
• Spatial Data Warehouse Models and Construction
• Spatial OLAP Implementation and Application
• Data Mining Basic Concepts and Techniques
• Spatial Data Mining
• Mining Spatial Association Rules.
  
• Spatial Classification and Prediction
• Spatial Data Clustering Analysis
• Conclusions and Future Research.

3
Motivation

• Data warehousing Integrating data from multiple
  sources into large warehouses and support on-line
  analytical processing and business decision
  making.
• Data mining (knowledge discovery in databases)
  Extraction of interesting knowledge
  (rules, regularities, patterns, constraints)
  from data in large databases.
• Necessity Data explosion problem ---
  computerized data collection tools and mature
  database technology lead to tremendous amounts of
  data stored in databases.
• We are drowning in data, but starving for
  knowledge!

4
Data Warehousing

• A data warehouse is a subject-oriented,
  integrated, time-variant, and nonvolatile
  collection of data in support of managements
  decision-making process. --- W. H. Inmon
• A data warehouse is
• A decision support database that is maintained
  separately from the organizations operational
  databases.
• It integrates data from multiple heterogeneous
  sources to support the continuing need for
  structured and /or ad-hoc queries, analytical
  reporting, and decision support.

5
Modeling Data Warehouses

• Modeling data warehouses dimensions
  measurements
• Star schema A single object (fact table) in the
  middle connected to a number of objects
  (dimension tables) radially.
• Snowflake schema A refinement of star schema
  where the dimensional hierarchy is represented
  explicitly by normalizing the dimension tables.
• Fact constellations Multiple fact tables share
  dimension tables.
• Storage of selected summary tables
• Independent summary table storing pre-aggregated
  data, e.g., total sales by product by year.
• Encoding aggregated tuples in the same fact table
  and the same dimension tables.

6
Example of Star Schema

Time Dimension Table
Sales Fact Table
Product Dimension Table
Many Time Attributes
Time_Key
Many Product Attributes
Product_Key
Store Dimension Table
Location Dimension Table
Store_Key
Many Location Attributes
Many Store Attributes
Location_Key
unit_sales
dollar_sales
Measurements
Yen_sales
7
Example of a Snowflake Schema
Supplier_Key

Sales Fact Table
Product Dimension Table
Time Dimension Table
Time_Key
Supplier_Key
Many Time Attributes
Product_Key
Product_Key
Store_Key
Store Dimension Table
Location Dimension Table
Location_Key
Many Store Attributes
Location_Key
unit_sales
Country
dollar_sales
Measurements
Location_Key
Yen_sales
Region
Location_Key
8
A Star-Net Query Model
Customer Orders
Shipping Method
Customer

CONTRACTS
AIR-EXPRESS
ORDER
TRUCK
PRODUCT LINE
Product
Time
DAILY
QTRLY
ANNUALY
PRODUCT ITEM
PRODUCT GROUP
DISTRICT
SALES PERSON
REGION
DISTRICT
COUNTRY
DIVISION
Geography
Organization
Promotion
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Construction of Data Cubes
All Amount Comp_Method, B.C.
Amount
0-20K
20-40K
60K-
sum
40-60K
Province
B.C.
Comp_Method
Prairies
Ontario
sum
Database
Discipline
...
sum

• Each dimension contains a hierarchy of values
  for one attribute
• A cube cell stores aggregate values, e.g., count,
  sum, max, etc.
• A sum cell stores dimension summation values.
• Sparse-cube technology and MOLAP/ROLAP
  integration.
• Chunk-based multi-way aggregation and
  single-pass computation.

10
Efficient Data Cube Computation Methods

• Data cube can be viewed as a lattice of cuboids
• The bottom-most cuboid is the base cube.
• The top most cuboid contains only one cell.
• Materialization of data cube
• Materialize every (cuboid), none, or some.
• Algorithms for selection of which cuboids to
  materialize.
• Based on size, sharing, and access frequency.
• Efficient cube computation methods
• ROLAP algorithms.
• Array-based cubing algorithm.

ALL
A
B
C
AB
BC
AC
ABC
AC
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OLAP On-Line Analytical Processing

• A multidimensional, LOGICAL view of the data.
• Interactive analysis of the data drill, pivot,
  slice_dice, filter.
• Summarization and aggregations at every dimension
  intersection.
• Retrieval and display of data in 2-D or 3-D
  crosstabs, charts, and graphs, with easy pivoting
  of the axes.
• Analytical modeling deriving ratios, variance,
  etc. and involving measurements or numerical data
  across many dimensions.
• Forecasting, trend analysis, and statistical
  analysis.
• Requirement Quick response to OLAP queries.

12
OLAP Architecture

• Logical architecture
• OLAP view multidimensional and logic
  presentation of the data in the data
  warehouse/mart to the business user.
• Data store technology The technology options of
  how and where the data is stored.
• Three services components
• data store services
• OLAP services, and
• user presentation services.
• Two data store architectures
• Multidimensional data store (MOLAP).
• Relational data store Relational OLAP (ROLAP).

13
Spatial Data Warehouse and Spatial OLAP

• Spatial Data Warehouse Integrated,
  subject-oriented, time-variant, and nonvolatile
  spatial data repository for data analysis and
  decision making.
• Spatial Data Integration A big issue.
• Spatial data cube Multidimensional spatial
  database.
• Non-spatial dimensions time, product,
  organization hierarchies.
• Spatial dimensions formed by geo-spatial
  hierarchies.
• Non-spatial (numerical) measurements
• Distributive, algebraic, holistic.
• Spatial Measurements
• Collection of spatial object pointers which may
  require spatial merge, overlay, or other
  operations.

14
Example Weather Pattern Analysis

• Input
• a map with about 3,000 weather probes scattered
  in B.C.
• daily data for temperature, precipitation, wind
  velocity, etc.
• concept hierarchies for all attributes
• Output
• a map that reveals patterns merged (similar)
  regions!
• Goals
• interactive analysis (drill-down, slice, dice,
  pivot, roll-up)
• fast response time
• minimizing storage space used
• Challenge a merged region may contain hundreds
  of primitive regions (polygons).

15
A Model of Spatial Data Warehouses

• Dimensions
• nonspatial
• (e.g. 25-30 degrees generalizes to hot)
• spatial-to-nonspatial
• (e.g. region B.C. generalizes to description
  western provinces)
• spatial-to-spatial
• (e.g. region Burnaby generalizes to region
  Lower Mainland)

• Measurements
• numerical
• distributive (e.g. count, sum)
• algebraic (e.g. average)
• holistic (e.g. median, rank)
• spatial
• collection of spatial pointers (e.g. pointers to
  all regions with 25-30 degrees in July)

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Star Model of a Spatial Data Warehouse

• Dimensions
• region_name
• time
• temperature
• precipitation
• Measurements
• region_map
• area
• count

Fact table
Dimension table
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Spatial Merge Pre- vs On-line Computation
Precomputing all too much storage space

On-line merge very expensive
18
Spatial Measurements Selective Materialization

• Methods for computation of spatial measurements
  in spatial data cube.
• Collect and store pointers to spatial objects in
  a spatial data cubeComputing on the fly ---
  expensive and slow.
• Saving all the possible combinations --- huge
  space overhead.
• Precompute and store rough approximations in a
  spatial data cube --- accuracy trade-off.
• Selective computation only materialize those
  which will be accessed frequently --- a
  reasonable choice.
• Cube lattice and granularity of merge-able
  spatial objects.
• Cuboid-level vs. cube cell level granularity.

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Computing Spatial Measurements

• Apply HRU96 greedy algorithm to select cuboids
• HRU96 algorithm has granularity on a cuboid
  level

• Finer granularity, on a cell level
• Only selected cells are materialized (not the
  whole cuboid)
• Factors in selections of cells
• access frequency
• size of a cell (number of merged objects)
• It could be better to save 1,3,4,7 than 1,3
• benefit for on-the-fly computationIf 1,3 is
  saved, it can be used for 1,3,6.
• Only neighboring objects are merged.

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Integration of Data Mining and Data Warehousing

• Data warehouse provides clean, integrated data
  for fruitful mining.
• Data mining provides powerful tools for analysis
  of data stored in data warehouses.
• OLAP can be viewed as data summarization and
  simple data mining.
• Data mining provides more analysis tools, e.g.,
  association, classification, clustering,
  pattern-directed, and trend analysis.
• Mining multi-level knowledge by integration with
  OLAP facilities mining in multiple data cubes.

21
Mining Different Kinds of Knowledge

• Characterization Generalize, summarize, and
  possibly contrast data characteristics, e.g.,
  dry vs. wet regions.
• Association Rules like inside(x, city) à
  near(x, highway).
• Classification Classify data based on the
  values in a classifying attribute, e.g., classify
  countries based on climate.
• Clustering Cluster data to form new classes,
  e.g., cluster houses to find distribution
  patterns.
• Trend and deviation analysis Find and
  characterize evolution trend, sequential
  patterns, similar sequences, and deviation data,
  e.g., housing market analysis.
• Pattern-directed analysis Find and characterize
  user-specified patterns in large databases, e.g.,
  volcanos on Mars.

22
Different Mining Tasks in Spatial DBs

• Spatial data mining tasks
• Spatial data characterization and comparison
• Spatial clustering analysis
• Spatial classification
• Spatial association
• Spatial pattern analysis
• Spatial concept hierarchies thematic vs.
  spatial.
• Thematic hierarchy e.g., agriculture (food
  (grain (corn, rice, ...), vegetable, fruit),
  others(...)).
• Spatial hierarchy, based on
• Spatial data structures (MBR, quad-tree
  R-tree).
• Spatial related semantics (geo-region
  classification).
• Clustering analysis (e.g., neighborhood or
  adjacent_to).

23
A Geo-Spatial Data Mining Query Language
GMQL

• Extension to Spatial SQL Egenhofer94.
• Support ad-hoc data mining queries.

• mine characteristic rules type of
  rule (characteristic, discriminant,
  association, clustering, classification)for
  Description of states along I 80 highway
• from us_hiway, states_census SQL like
  from, where clauses
• where states_census.obj intersects us_hiway.obj
  high level concepts and and highway "I 80
  spatial joins may be usedwith respect to
  states_census.obj, state_name, pop90,
  capita_income list of relevant attributes
• set attribute threshold 51 for state_name
  thresholds for rules filtration

24
Background Knowledge for Data Mining

• Conceptual "hierarchies" and generalization
  operators.
• Instance-based freshman, ..., senior Ì
  undergraduate.
• Schema-based address(city, province, country).
• Rule-based good(x) undergraduate(x) Ù
  gpa(x) ³ 3.5.
• Operation-based aggregation, approximation,
  clustering, etc.
• Where to get such background knowledge?
• Implicitly stored in databases, such as address.
• Explicitly defined by experts, such as "physics
  Ì science".
• Formed with different attribute combinations,
• food(category, brand, content _spec, package
  _size, price).
• Generated automatically by data distribution
  analysis.
• May need dynamic adjustment for a particular set
  of data.
• Choose from multiple hierarchies or try them in
  parallel.

25
Automatic Generation of Numeric Hierarchies

Count
Amount
2000-97000
2000-16000
16000-97000
2000-12000
12000-16000
16000-23000
23000-97000
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Spatial OLAP (Characterization)

• Viewing data from different angles
• Summarization on multiple concept levels

27
Mining Discriminant Rules

• Discrimination Comparison of two or more classes
• Strategy
• Collect the relevant data respectively into
  the target class and the contrasting class
• Generalize both classes to the same high level
  concepts,
• Compare tuples with the same high level
  descriptions,
• Present for every tuple its description and two
  numbers
• support - distribution within single class
• comparison - distribution between classes
• Highlight the tuples with strong discriminant
  features
• Interestingness
• Different measures of interestingness,e.g.
  consider also the sizes of different classes

28
Spatial OLAP (Comparison)

• Comparing different classes of data

Population increases faster in the western
part. Drill down, and look at different
dimensions to get explanation!!
29
Mining Association Rules

• Association Finding association among a set of
  attributes and their values.
• Applications pattern association, market
  analysis, etc.
• Examples.
• milk bread 5, 60
• tire Ù auto_accessories auto_services 2,
  80
• Methods for mining associations
• Apriori ( Agrawal Srikant94)
• Partition technique (Savasere, Omiecinski,
  Navathe95)
• Sampling (Toivonen96)

30
Spatial Associations
FIND SPATIAL ASSOCIATION RULE DESCRIBING "Golf
Course" FROM Washington_Golf_courses,
Washington WHERE CLOSE_TO(Washington_Golf_courses.
Obj, Washington.Obj, "3 km") AND
Washington.CFCC ltgt "D81" IN RELEVANCE TO
Washington_Golf_courses.Obj, Washington.Obj, CFCC
SET SUPPORT THRESHOLD 0.5
31
Spatial Associations Hierarchy of Spatial
Relationships

• Spatial association Association relationship
  containing spatial predicates, e.g., close_to,
  intersect, contains, etc.
• Topological relations
• intersects, overlaps, disjoint, etc.
• Spatial orientations
• left_of, west_of, under, etc.
• Distance information
• close_to, within_distance, etc.
• Hierarchy of spatial relationship
• g_close_to near_by, touch, intersect, contain,
  etc.
• First search for rough relationship and then
  refine it.

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Efficient Mining of Spatial Associations

• Two-step computation of spatial associations
• Step 1 rough spatial computation as a filter
• MBR or R-tree rough estimation.
• Step2 Detailed spatial algorithm as refinement
• apply only to those pairs which have passed the
  rough spatial association testing (no less than
  min_support).
• Multi-dimensional mining
• explore association relationships at any selected
  granularity level
• perform drill-down and roll-up on any dimension.

33
Example Spatial Association Rule Mining

• What kinds of spatial objects are close to each
  other in B.C.?
• Kinds of objects cities, water, forests,
  usa_boundary, mines, etc.
• Rules mined
• is_a(x, large_town) intersect(x, highway)
  adjacent_to(x, water). 7, 85
• is_a(x, large_town) adjacent_to(x,
  georgia_strait) close_to(x, u.s.a.). 1, 78
• Mining method Ariori multi-level association
  geo- spatial algorithms (from rough to high
  precision).

34
Data Classification

• Data categorization based on a set of training
  objects.
• Applications credit approval, target marketing,
  medical diagnosis, treatment effectiveness
  analysis, etc.
• Example classify a set of diseases and provide
  the symptoms which describe each class or
  subclass.
• The classification task Based on the features
  present in the class_labeled training data,
  develop a description or model for each class.
  It is used for
• classification of future test data,
• better understanding of each class, and
• prediction of certain properties and behaviors.
• Data classification methods Decision-trees
  (e.g., ID3, C4.5), statistics, neural networks,
  rough sets, etc.

35
A Decision-Tree Based Classification Method

• A decision tree
• ID-3 and C4.5 (Quinlan93) A top-down decision
  tree generation algorithm.
• At start, all the training examples are at the
  root.
• Partition examples recursively based on selected
  attributes.
• Attribute selection Maximizing an information
  gain measure, i.e., favoring the partitioning
  which makes the majority of examples belong to a
  single class.

outlook
sunny
rain
overcast
windy
humidity
P
N
P
N
P
36
Scalable Classification Methods

• Scalability of decision-tree classification
  algorithms.
• Previous approaches
• Incremental tree construction (Quinlan86)
  total cost is high.
• Data sampling and discretizing continuous
  attributes
• (Cattlet91) still in main memory.
• Data partition and merge of parallel partition
  (Chan and Stolfo91) reduced classification
  accuracy.
• SLIQ SPRINT (Mehta et al.96, Shafer et
  al.96) disk-based
• Decision-tree construction algorithms.
• Techniques Pre-sorting, breadth_first
  tree-growing, and tree-pruning.

37
Generalization-Based Decision-Tree Induction

• Integration of generalization with decision-tree
  induction.
• Classification at primitive concept levels, e.g.,
  precise
• temperature, humidity, outlook, etc.
• Weakness low-level concepts, scattered classes,
  bushy
• classification-trees, semantic
  interpretation problems.
• Classification at high or medium concept levels
• may lead to imprecise classification.
• Medium level generalization adjustment
• Generalize to intermediate concept level(s).
• Merge and split concept levels for better class
  representation and classification accuracy.
• Efficiency Analysis performed in compressed,
  generalized relations.

38
Mining Classification Rules

• Classification Based on the features present in
  the class_labeled training data, develop a
  description or model for each class.
• Applications credit approval, target marketing,
  medical diagnosis, treatment effectiveness
  analysis, etc.
• Example classify a set of diseases and provide
  the symptoms which describe each class or
  subclass.

39
Spatial Classification

• Generalization-based induction
• Interactive classification

40
Predictive Modeling in Databases

• Predictive modeling Predict data values or
  construct generalized linear models based on
  the database data.
• One can only predict value ranges or category
  distributions.
• Method outline
• Minimal generalization
• Attribute relevance analysis
• Generalized linear model construction
• Prediction.
• Determine the major factors which influence the
  prediction.
• Data relevance analysis uncertainty measurement,
  entropy analysis, expert judgement, etc.
• Multi-level prediction drill-down and roll-up
  analysis.

41
Spatial Prediction and Trend Analysis

• Spatial trend predictive modeling (Ester et
  al97)
• Discover centers local maximal of some
  non-spatial attribute.
• Determine the (theoretical) trend of some
  non-spatial attribute, when moving away from the
  centers.
• Discover deviations (from the theoretical trend).
• Explain the deviations.
• Example Trend of unemployment rate change
  according to the distance to Munich.
• Similar modeling can be used to study trend of
  temperature with the altitude, degree of
  pollution in relevance to the regions of
  population density, etc.

42
Data Clustering Analysis

• Data clustering (unsupervised learning)
  Cluster objects
• into classes, based on their features, which
  maximize intraclass similarity and minimize
  interclass similarity.
• Probability-based vs. distance-based clustering
  analysis.
• Typical probability-based clustering analysis
  algorithms
• COBWEB (Fisher87) Incremental concept
  formation.
• Category utility measurement (probability of each
  concepts occurrence)
• Top-down, incremental, hierarchical organization
  of concepts.
• CLASSIT (Gennari89) extend it to real-valued
  data.
• Typical distance-based clustering analysis
  algorithms
• Statistics-based, k-means, k-medoids, nearest
  neighbors.

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Distance-Based Spatial Clustering Analysis

• Statistical approaches scan data frequently,
  iterative
• optimization, hierarchical clustering, etc.
• CLARANS (Ng Han94) randomized search
  (sampling)
• PAM (a distance-based clustering
  algorithm).
• DASCAN (Ester et al.96) density-based
  clustering using spatial data structures
  (R-tree).
• BIRCH (Zhang et al.96) Balanced iterative
  reducing and
• clustering using hierarchies.
• Focus on densely occupied portions of the data
  space.
• Measurement reflects the natural closeness of
  points.
• A height-balanced tree (CF-tree) is used for
  clustering.
• Describe aggregate proximity relationships (Knorr
  Ng96).

44
Spatial Clustering

• How can we cluster points?
• What are the distinct features of the clusters?

There are more customers with university degrees
in clusters located in the West. Thus, we can
use different marketing strategies!
45
Data and Knowledge Visualization

• Visualization of characteristic and discriminant
  rules
• tables cubes bar/pie charts, curves,
  surfaces, etc.
• Visualization of association rules
• Association rule graph Nodes for large
  1-itemset, lines for large 2-items sets, arrows
  for implication strength.
• Association matrix support/confidence
  size/color in cells.
• Cluster analysis viewing clusters and their
  characteristics.
• Classification colored decision trees.
• Prediction curves, pie charts, and relevance
  analysis results.
• Deviation analysis boxplots (quartiles, median)
  and outliers.
• Visual impression of large data mining results
• arrange and color data items as pixels (Keim et
  al.94)

46
Visual Data Mining (ref. D. Keim SIGMOD96
Tutorial)

• Data visualization and exploratory analysis
• Interactive, usually undirected search for
  structures, trends, etc.
• Typical data visualization techniques
• Geometric techniques, icon-based techniques,
  pixel-oriented techniques, hierarchical
  techniques, graph-based techniques,
  3D-techniques, dynamic techniques, and hybrid
  techniques.
• Database visualization systems
• Statistics-oriented systems, visualization-oriente
  d systems, database-oriented systems and special
  purpose systems.
• Visual database exploration is another powerful
  approach to data mining, especially spatial data
  mining.

47
Data Mining Interfaces

• Interactive mining versus a data mining
  language.
• Specification of data mining tasks.
• Data sets any sets of data in databases
• Mining task specification kinds of knowledge or
  forms of rules to be mined.
• Background knowledge (e.g., concept
  hierarchies) specification and manipulation.
• Interestingness measurement significance,
  confidence, thresholds, concept levels, etc.
• Transformation and manipulation of output
  results.
• Roll-up vs. drill-down.
• Multiple output forms generalized relations,
  crosstabs, charts, curves, and other visual
  outputs.

48
GeoMiner Graphical User Interface
49
Systems for Data Warehousing and Data Mining

• Systems for Data Warehousing
• Arbor Software Essbase
• Oracle (IRI) Express
• Cognos PowerPlay
• Redbrick Systems Redbrick Warehouse
• Microstrategy DSS/Server
• Systems or Research Prototypes for Data Mining
• IBM QUEST (Intelligent Miner)
• Silicon Graphics MineSet
• Integral Solutions Ltd. Clementine
• Information Discovery Inc. Data Mining Suite
• SFU (DBTech) DBMiner, GeoMiner
• Rutger DataMine, GMD Explora, U Munich VisDB

50
Conclusions

• Data warehousing and data mining
• A rich, promising, young field with broad
  applications and many challenging research
  issues.
• Imminent task spatial database analysis --- from
  spatial data manipulation to on-line spatial
  analytical processing (Spatial OLAP) and spatial
  data mining.
• Spatial data cube construction fine granularity
  analysis.
• Multiple spatial data mining tasks
  Characterization, association, classification,
  clustering, sequence and pattern analysis,
  prediction, etc.
• Integration of data mining with OLAP OLAP-based
  spatial data mining.
• Integration of spatial analysis methods, spatial
  query processing methods, and spatial indexing
  techniques.

51
Future Research

• Foundation of spatial data warehousing and data
  mining.
• Implementation methods
• Efficient construction of spatial data cubes.
• A set of well-tuned spatial data mining
  operators.
• Spatial data and knowledge visualization tools.
• Integration of multiple mining tasks with OLAP
  functions.
• New spatial indexing techniques for spatial data
  warehousing and spatial mining.
• New spatial data mining methodologies
  Statistical tools, neural nets, and ad-hoc
  query-based mining, etc.
• Mining spatiotemporal data, raster data, and
  integration with existing spatial analysis
  techniques.

52
References

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  Kuijpers.
• A Theory of Spatio-Temporal Database. Computer
  Science Dept., North Dakota State University
  (2000)
•  
• 2 Martin Ester, Hans-Peter Kriegel, Jörg
  Sander.Algorithms and Applications for Spatial
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  Knowledge Discovery, 2001.
•  
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  Kriegel, Jörg Sander. Algorithms for
  Characterization and Trend Detection in Spatial
  Databases, International Conference on Knowledge
  Discovery and Data Mining (KDD-98)
•  
• 4 Jan Paredaens, Bart Kuijpers. Data Models and
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  Explorations (1999)
•  
• 5 Hans-Peter Kriegel, Thomas Brinkhoff, Ralf
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•  
• 6 Usama Fayyad, Gregory Piatetsky-Shapiro, and
  Padhraic Smyth. From Data Mining to Knowledge
  Discovery in Databases. AI MAGAZINE (1999)
•  
• 7 Ramakrishnan Srikant, Rakesh Agrawal. Mining
  Quantitative Association Rules in Large
  Relational Tables. VLDB (1996)
•  
• 8 Krzysztof Koperski,  A Progressive
  Refinement  Approach to Spatial Data Mining. SFU
  PhD Thesis (1999)

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• Thank you !!!