CSE 634 Data Mining Techniques

1
CSE 634 Data Mining Techniques

• CLUSTERING
• Part 2( Group no 1 )
• By Anushree Shibani Shivaprakash Fatima
  Zarinni
• Spring 2006
• Professor Anita WasilewskaSUNY Stony Brook

2
References

• Jiawei Han and Michelle Kamber. Data Mining
  Concept and Techniques (Chapter8). Morgan
  Kaufman, 2002.
• M. Ester, H.P. Kriegel, J. Sander, and X. Xu. A
  density-based algorithm for discovering clusters
  in large spatial databases. KDD'96.
  http//ifsc.ualr.edu/xwxu/publications/kdd-96.pdf
• How to explain hierarchical clustering.
  http//www.analytictech.com/networks/hiclus.htm
• Tian Zhang, Raghu Ramakrishnan, Miron Livny.
  Birch An efficient data clustering method for
  very large databases
• Data mining- Margaret H. Dunham
• http//cs.sunysb.edu/cse634/ Presentation 9
  Cluster Analysis

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Introduction

• Major clustering methods
• Partitioning methods
• Hierarchical methods
• Density-based methods
• Grid-based methods

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Hierarchical methods

• Here we group data objects into a tree of
  clusters.
• There are two types of hierarchical clustering
• Agglomerative hierarchical
• clustering.
• Divisive hierarchical clustering

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Agglomerative hierarchical clustering

• Group data objects in a bottom-up fashion.
• Initially each data object is in its own cluster.
• Then we merge these atomic clusters into larger
  and larger clusters, until all of the objects are
  in a single cluster or until certain termination
  conditions are satisfied.
• A user can specify the desired number of clusters
  as a termination condition.

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Divisive hierarchical clustering

• Groups data objects in a top-down fashion.
• Initially all data objects are in one cluster.
• We then subdivide the cluster into smaller and
  smaller clusters, until each object forms cluster
  on its own or satisfies certain termination
  conditions, such as a desired number of clusters
  is obtained.

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AGNES DIANA

• Application of AGNES( AGglomerative NESting) and
  DIANA( Divisive ANAlysis) to a data set of five
  objects, a, b, c, d, e.

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AGNES-Explored

• Given a set of N items to be clustered, and an
  NxN distance (or similarity) matrix, the basic
  process of Johnson's (1967) hierarchical
  clustering is this
• Start by assigning each item to its own cluster,
  so that if you have N items, you now have N
  clusters, each containing just one item. Let the
  distances (similarities) between the clusters
  equal the distances (similarities) between the
  items they contain.
• Find the closest (most similar) pair of clusters
  and merge them into a single cluster, so that now
  you have one less cluster.

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AGNES

• Compute distances (similarities) between the new
  cluster and each of the old clusters.
• Repeat steps 2 and 3 until all items are
  clustered into a single cluster of size N.
• Step 3 can be done in different ways, which is
  what distinguishes single-link from complete-link
  and average-link clustering

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Similarity/Distance metrics

• single-link clustering, distance
• shortest distance
• complete-link clustering, distance
• longest distance
• average-link clustering, distance
• average distance
• from any member of one cluster to any member of
  the other cluster.

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Single Linkage Hierarchical Clustering

• Say Every point is its own cluster

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Single Linkage Hierarchical Clustering

• Say Every point is its own cluster
• Find most similar pair of clusters

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Single Linkage Hierarchical Clustering

• Say Every point is its own cluster
• Find most similar pair of clusters
• Merge it into a parent cluster

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Single Linkage Hierarchical Clustering

• Say Every point is its own cluster
• Find most similar pair of clusters
• Merge it into a parent cluster
• Repeat

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Single Linkage Hierarchical Clustering

• Say Every point is its own cluster
• Find most similar pair of clusters
• Merge it into a parent cluster
• Repeat

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DIANA (Divisive Analysis)

• Introduced in Kaufmann and Rousseeuw (1990)
• Inverse order of AGNES
• Eventually each node forms a cluster on its own

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Overview

• Divisive Clustering starts by placing all objects
  into a single group. Before we start the
  procedure, we need to decide on a threshold
  distance.
• The procedure is as follows
• The distance between all pairs of objects within
  the same group is determined and the pair with
  the largest distance is selected.

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Overview-contd

• This maximum distance is compared to the
  threshold distance.
• If it is larger than the threshold, this group is
  divided in two. This is done by placing the
  selected pair into different groups and using
  them as seed points. All other objects in this
  group are examined, and are placed into the new
  group with the closest seed point. The procedure
  then returns to Step 1.
• If the distance between the selected objects is
  less than the threshold, the divisive clustering
  stops.
• To run a divisive clustering, you simply need to
  decide upon a method of measuring the distance
  between two objects.

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DIANA- Explored

• In DIANA, a divisive hierarchical clustering
  method, all of the objects form one cluster.
• The cluster is split according to some principle,
  such as the minimum Euclidean distance between
  the closest neighboring objects in the cluster.
• The cluster splitting process repeats until,
  eventually, each new cluster contains a single
  object or a termination condition is met.

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Difficulties with Hierarchical clustering

• It encounters difficulties regarding the
  selection of merge and split points.
• Such a decision is critical because once a group
  of objects is merged or split, the process at the
  next step will operate on the newly generated
  clusters.
• It will not undo what was done previously.
• Thus, split or merge decisions, if not well
  chosen at some step, may lead to low-quality
  clusters.

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Solution to improve Hierarchical clustering

• One promising direction for improving the
  clustering quality of hierarchical methods is to
  integrate hierarchical clustering with other
  clustering techniques. A few such methods are
• Birch
• Cure
• Chameleon

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BIRCH An Efficient Data Clustering Method for
Very Large Databases

• Paper by

• Miron Livny
• Computer Sciences Dept.
• University of Wisconsin- Madison
• miron_at_cs.wisc.edu

• Raghu Ramakrishnan
• Computer Sciences Dept.
• University of Wisconsin- Madison
• raghu_at_cs.wisc.edu

Tian Zhang Computer Sciences Dept. University of
Wisconsin- Madison zhang_at_cs.wisc.edu
In Proceedings of the International Conference
Management of Data (ACM-SIGMOD), pages 103-114,
Montreal, Canada, June, 1996.
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Reference For Paper

• www2.informatik.huberlin.de/wm/mldm2004/zhang96bir
  ch.pdf

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Birch (Balanced Iterative Reducing and Clustering
Using Hierarchies)

• A hierarchical clustering method.
• It introduces two concepts
• Clustering feature
• Clustering feature tree (CF tree)
• These structures help the clustering method
  achieve good speed and scalability in large
  databases.

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Clustering Feature Definition

• Given N d-dimensional data points in a cluster
  Xi where i 1, 2, , N,
• CF (N, LS, SS)
• N is the number of data points in the cluster,
• LS is the linear sum of the N data points,
• SS is the square sum of the N data points.

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Clustering feature concepts

• Each record (data object) is a tuple of values of
  attributes and here is called a vector.
• Here is a database.
• We define
• (Vi1, Vid) Oi
• N N N N
• LS ? Oi (?Vi1, ? Vi2, ?Vid)
• i1 i1 i1 i 1

Linear Sum Definition
Definition
Name
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Square sum

• N N N
  N
• SS ? Oi2 ( ?Vi12, ?Vi22 ?Vid2)
• i 1 i1 i1
  i1

Definition
Name
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Example of a case

• Assume N 5 and d 2
• Linear Sum
• 5 5 5
• LS ? Oi (?Vi1, ? Vi2)
• i1 i1 i1
• Square Sum
• 5 5
• SS ( ?Vi12), ?Vi22)
• i1 i1

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Example 2
Clustering feature CF( N, LS, SS) N 5 LS
(16, 30) SS ( 54, 190)
CF (5, (16,30),(54,190))
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CF-Tree

• A CF-tree is a height-balanced tree with two
  parameters branching factor (B for nonleaf node
  and L for leaf node) and threshold T.
• The entry in each nonleaf node has the form CFi,
  childi
• The entry in each leaf node is a CF each leaf
  node has two pointers prev' andnext'.
• The CF tree is basically a tree used to store all
  the clustering features.

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CF Tree
Root
Non-leaf node
CF1
CF3
CF2
CF5
child1
child3
child2
child5
Leaf node
Leaf node
CF1
CF2
CF6
prev
next
CF1
CF2
CF4
prev
next
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BIRCH Clustering

• Phase 1 scan DB to build an initial in-memory CF
  tree (a multi-level compression of the data that
  tries to preserve the inherent clustering
  structure of the data)
• Phase 2 use an arbitrary clustering algorithm to
  cluster the leaf nodes of the CF-tree

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BIRCH Algorithm Overview

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Summary of Birch

• Scales linearly- with a single scan you get good
  clustering and the quality of clustering improves
  with a few additional scans.
• It handles noise (data points that are not part
  of the underlying pattern) effectively.

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Density-Based Clustering Methods

• Clustering based on density, such as
  density-connected points instead of distance
  metric.
• Cluster set of density connected points.
• Major features
• Discover clusters of arbitrary shape
• Handle noise
• Need density parameters as termination
  condition-
• (when no new objects can be added to the
  cluster.)
• Example
• DBSCAN (Ester, et al. 1996)
• OPTICS (Ankerst, et al 1999)
• DENCLUE (Hinneburg D. Keim 1998)

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Density-Based Clustering Background

• Eps neighborhood The neighborhood within a
  radius Eps of a given object
• MinPts Minimum number of points in an
  Eps-neighborhood of that object.
• Core object If the Eps neighborhood contains at
  least a minimum number of points Minpts, then the
  object is a core object
• Directly density-reachable A point p is directly
  density-reachable from a point q wrt. Eps, MinPts
  if
• 1) p is within the Eps neighborhood of q
• 2) q is a core object

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Figure showing the density reachability and
density connectivity in density based clustering

• M, P, O, R and S are core objects since each is
  in an Eps neighborhood containing at least 3
  points

Minpts 3 Epsradius of the circles
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Directly density reachable

• Q is directly density reachable from M. M is
  directly density reachable from P and vice versa.

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Indirectly density reachable

• Q is indirectly density reachable from P since Q
  is directly density reachable from M and M is
  directly density reachable from P. But, P is not
  density reachable from Q since Q is not a core
  object.

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Core, border, and noise points

• DBSCAN is a density-based algorithm.
• Density number of points within a specified
  radius (Eps)
• A point is a core point if it has more than a
  specified number of points (MinPts) within Eps
• These are points that are at the interior of a
  cluster.
• A border point has fewer than MinPts within Eps,
  but is in the neighborhood of a core point.
• A noise point is any point that is not a core
  point nor a border point.

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DBSCAN (Density based Spatial clustering of
Application with noise) The Algorithm

• Arbitrary select a point p
• Retrieve all points density-reachable from p wrt
  Eps and MinPts.
• If p is a core point, a cluster is formed.
• If p is a border point, no points are
  density-reachable from p and DBSCAN visits the
  next point of the database.
• Continue the process until all of the points have
  been processed.

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Conclusions

• We discussed two hierarchical clustering methods
  Agglomerative and Divisive.
• We also discussed Birch- a hierarchical
  clustering which produces good clustering over a
  single scan and with a few additional scans you
  get better clustering.
• DBSCAN is a density based clustering algorithm
  and through this algorithm we discover clusters
  of arbitrary shapes. Distance is not the metric
  unlike the case of hierarchical methods.

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GRID-BASED CLUSTERING METHODS

• This is the approach in which we quantize
  space into a finite number of cells that form a
  grid structure on which all of the operations for
  clustering is performed.
• So, for example assume that we have a set of
  records and we want to cluster with respect to
  two attributes, then, we divide the related space
  (plane), into a grid structure and then we find
  the clusters.

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Salary (10,000)
Our space is this plane
8
7
6
5
4
3
2
1
0
20 30 40
50 60
Age
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Techniques for Grid-Based Clustering

• The following are some techniques that are used
  to perform Grid-Based Clustering
• CLIQUE (CLustering In QUest.)
• STING (STatistical Information Grid.)
• WaveCluster

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Looking at CLIQUE as an Example

• CLIQUE is used for the clustering of
  high-dimensional data present in large tables.
  By high-dimensional data we mean records that
  have many attributes.
• CLIQUE identifies the dense units in the
  subspaces of high dimensional data space, and
  uses these subspaces to provide more efficient
  clustering.

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Definitions That Need to Be Known

• Unit After forming a grid structure on the
  space, each rectangular cell is
  called a Unit.
• Dense A unit is dense, if the fraction of
  total data points contained in the
  unit exceeds the input model
  parameter.
• Cluster A cluster is defined as a maximal set of
  connected dense units.

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How Does CLIQUE Work?

• Let us say that we have a set of records that we
  would like to cluster in terms of n-attributes.
• So, we are dealing with an n-dimensional space.
• MAJOR STEPS
• CLIQUE partitions each subspace that has
  dimension 1 into the same number of equal length
  intervals.
• Using this as basis, it partitions the
  n-dimensional data space into non-overlapping
  rectangular units.

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CLIQUE Major Steps (Cont.)

• Now CLIQUES goal is to identify the dense
  n-dimensional units.
• It does this in the following way
• CLIQUE finds dense units of higher dimensionality
  by finding the dense units in the subspaces.
• So, for example if we are dealing with a
  3-dimensional space, CLIQUE finds the dense units
  in the 3 related PLANES (2-dimensional
  subspaces.)
• It then intersects the extension of the subspaces
  representing the dense units to form a candidate
  search space in which dense units of higher
  dimensionality would exist.

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CLIQUE Major Steps. (Cont.)

• Each maximal set of connected dense units is
  considered a cluster.
• Using this definition, the dense units in the
  subspaces are examined in order to find clusters
  in the subspaces.
• The information of the subspaces is then used to
  find clusters in the n-dimensional space.
• It must be noted that all cluster boundaries are
  either horizontal or vertical. This is due to the
  nature of the rectangular grid cells.

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Example for CLIQUE

• Let us say that we want to cluster a set of
  records that have three attributes, namely,
  salary, vacation and age.
• The data space for the this data would be
  3-dimensional.

vacation
age
salary
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Example (Cont.)

• After plotting the data objects, each dimension,
  (i.e., salary, vacation and age) is split into
  intervals of equal length.
• Then we form a 3-dimensional grid on the space,
  each unit of which would be a 3-D rectangle.
• Now, our goal is to find the dense 3-D
  rectangular units.

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Example (Cont.)

• To do this, we find the dense units of the
  subspaces of this 3-d space.
• So, we find the dense units with respect to age
  for salary. This means that we look at the
  salary-age plane and find all the 2-D rectangular
  units that are dense.
• We also find the dense 2-D rectangular units for
  the vacation-age plane.

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Example 1
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Example (Cont.)

• Now let us try to visualize the dense units of
  the two planes on the following 3-d figure

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Example (Cont.)

• We can extend the dense areas in the vacation-age
  plane inwards.
• We can extend the dense areas in the salary-age
  plane upwards.
• The intersection of these two spaces would give
  us a candidate search space in which
  3-dimensional dense units exist.
• We then find the dense units in the
  salary-vacation plane and we form an extension of
  the subspace that represents these dense units.

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Example (Cont.)

• Now, we perform an intersection of the candidate
  search space with the extension of the dense
  units of the salary-vacation plane, in order to
  get all the 3-d dense units.
• So, What was the main idea?
• We used the dense units in subspaces in order to
  find the dense units in the 3-dimensional space.
• After finding the dense units, it is very easy to
  find clusters.

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Reflecting upon CLIQUE

• Why does CLIQUE confine its search for dense
  units in high dimensions to the intersection of
  dense units in subspaces?
• Because the Apriori property employs prior
  knowledge of the items in the search space so
  that portions of the space can be pruned.
• The property for CLIQUE says that if a
  k-dimensional unit is dense then so are its
  projections in the (k-1) dimensional space.

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Strength and Weakness of CLIQUE

• Strength
• It automatically finds subspaces of the highest
  dimensionality such that high density clusters
  exist in those subspaces.
• It is quite efficient.
• It is insensitive to the order of records in
  input and does not presume some canonical data
  distribution.
• It scales linearly with the size of input and has
  good scalability as the number of dimensions in
  the data increases.
• Weakness
• The accuracy of the clustering result may be
  degraded at the expense of simplicity of the
  simplicity of this method.

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STING A Statistical Information Grid Approach to
Spatial Data Mining

• Paper by

Jiong Yang Department of Computer
Science University of California, Los Angeles CA
90095, U.S.A. jyang_at_cs.ucla.edu
Richard Muntz Department of Computer
Science University of California, Los Angeles CA
90095, U.S.A. muntz_at_cs.ucla.edu
Wei Wang Department of Computer
Science University of California, Los Angeles CA
90095, U.S.A. weiwang_at_cs.ucla.edu
VLDB Conference Athens, Greece, 1997
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Reference For Paper

• http//georges.gardarin.free.fr/Cours_XMLDM_Maste
  r2/Sting.PDF

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Definitions That Need to Be Known

• Spatial Data
• Data that have a spatial or location component.
• These are objects that themselves are located in
  physical space.
• Examples My house, lake Geneva, New York City,
  etc.
• Spatial Area
• The area that encompasses the locations of all
  the spatial data is called spatial area.

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STING (Introduction)

• STING is used for performing clustering on
  spatial data.
• STING uses a hierarchical multi resolution grid
  data structure to partition the spatial area.
• STINGS big benefit is that it processes many
  common region oriented queries on a set of
  points, efficiently.
• We want to cluster the records that are in a
  spatial table in terms of location.
• Placement of a record in a grid cell is
  completely determined by its physical location.

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Hierarchical Structure of Each Grid Cell

• The spatial area is divided into rectangular
  cells. (Using latitude and longitude.)
• Each cell forms a hierarchical structure.
• This means that each cell at a higher level is
  further partitioned into 4 smaller cells in the
  lower level.
• In other words each cell at the ith level (except
  the leaves) has 4 children in the i1 level.
• The union of the 4 children cells would give back
  the parent cell in the level above them.

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Hierarchical Structure of Cells (Cont.)

• The size of the leaf level cells and the number
  of layers depends upon how much granularity the
  user wants.
• So, Why do we have a hierarchical structure for
  cells?
• We have them in order to provide a better
  granularity, or higher resolution.

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A Hierarchical Structure for Sting Clustering
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Statistical Parameters Stored in each Cell

• For each cell in each layer we have attribute
  dependent and attribute independent parameters.
• Attribute Independent Parameter
• Count number of records in this cell.
• Attribute Dependent Parameter
• (We are assuming that our attribute values are
  real numbers.)

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Statistical Parameters (Cont.)

• For each attribute of each cell we store the
  following parameters
• M ? mean of all values of each attribute in this
  cell.
• S ? Standard Deviation of all values of each
  attribute in this cell.
• Min ? The minimum value for each attribute in
  this cell.
• Max ? The maximum value for each attribute in
  this cell.
• Distribution ? The type of distribution that the
  attribute value in this cell follows. (e.g.
  normal, exponential, etc.) None is assigned to
  Distribution if the distribution is unknown.

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Storing of Statistical Parameters

• Statistical information regarding the attributes
  in each grid cell, for each layer are
  pre-computed and stored before hand.
• The statistical parameters for the cells in the
  lowest layer is computed directly from the values
  that are present in the table.
• The Statistical parameters for the cells in all
  the other levels are computed from their
  respective children cells that are in the lower
  level.

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How are Queries Processed ?

• STING can answer many queries, (especially region
  queries) efficiently, because we dont have to
  access full database.
• How are spatial data queries processed?
• We use a top-down approach to answer spatial
  data queries.
• Start from a pre-selected layer-typically with a
  small number of cells.
• The pre-selected layer does not have to be the
  top most layer.
• For each cell in the current layer compute the
  confidence interval (or estimated range of
  probability) reflecting the cells relevance to
  the given query.

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Query Processing (Cont.)

• The confidence interval is calculated by using
  the statistical parameters of each cell.
• Remove irrelevant cells from further
  consideration.
• When finished with the current layer, proceed to
  the next lower level.
• Processing of the next lower level examines only
  the remaining relevant cells.
• Repeat this process until the bottom layer is
  reached.

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Different Grid Levels during Query Processing.
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Sample Query Examples

• Assume that the spatial area is the map of the
  regions of Long Island, Brooklyn and Queens.
• Our records represent apartments that are
  present throughout the above region.
• Query Find all the apartments that are for
  rent near Stony Brook University that have a rent
  range of 800 to 1000
• The above query depend upon the parameter near.
  For our example near means within 15 miles of
  Stony Brook University.

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Advantages and Disadvantages of STING

• ADVANTAGES
• Very efficient.
• The computational complexity is O(k) where k is
  the number of grid cells at the lowest level.
  Usually
• k ltlt N, where N is the number of records.
• STING is a query independent approach, since
  statistical information exists independently of
  queries.
• Incremental update.
• DISADVANTAGES
• All Cluster boundaries are either horizontal or
  vertical, and no diagonal boundary is selected.

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• Thank you !