1 Visualization and Data Mining techniques By- Group number- 14 Chidroop Madhavarapu(105644921) Deepanshu Sandhuria(105595184) Data Mining CSE 634 Prof

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Visualization and Data Mining techniques

By- Group number- 14

Chidroop Madhavarapu(105644921)Deepanshu Sandhuria(105595184)

Data Mining CSE 634Prof. Anita Wasilewska

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References

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdf

http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdf

http://www.geocities.com/anand_palm/

http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.pdf

http://www.cs.umn.edu/Research/shashi-group/

http://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdf

http://www.cs.umn.edu/research/shashi-group/alan_planb.pdf

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf




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Motivation

Visualization for Data Mining

• Huge amounts of information

• Limited display capacity of output devices

Visual Data Mining (VDM) is a new approach forexploring very large data sets, combining traditionalmining methods and information visualization

techniques.

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Why Visual Data Mining

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Why Visual Data Mining

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VDM Approach

VDM takes advantage of both,

The power of automatic calculations, and

The capabilities of human processing. Human perception offers phenomenal

abilities to extract structures from pictures.

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Levels of VDM

No or very limited integration Corresponds to the application of either traditional

information visualization or automated data mining methods.

Loose integration Visualization and automated mining methods are applied

sequentially.

The result of one step can be used as input for another step.

Full integration Automated mining and visualization methods applied in

parallel.

Combination of the results.

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Methods of Data Visualization

Different methods are available for visualization of data

based on type of data

Data can be

Univariate

Bivariate

Multivariate

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Univariate data

Measurement of single quantitative variable

Characterize distribution

Represented using following methods

Histogram

Pie Chart

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Histogram

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Pie Chart

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Bivariate Data

Constitutes of paired samples of two quantitative variables

Variables are related


Scatter plots

Line graphs

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Scatter plots

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Line graphs

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Multivariate Data

Multi dimensional representation of multivariate data


Icon based methods

Pixel based methods

Dynamic parallel coordinate system

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Icon based Methods

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Pixel Based Methods

Approach:

Each attribute value is represented by one colored pixel (the value ranges of the attributes are mapped to a fixed color map).

The values of each attribute are presented in separate sub windows.

Examples: Dense Pixel Displays

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Dense Pixel Display

Approach: Each attribute value is represented by one

colored pixel (the value ranges of the attributes are mapped to a fixed color map).

Different attributes are presented in separate sub windows.

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Visual Data Mining: Framework and Algorithm Development

Ganesh, M., Han, E.H., Kumar, V., Shekar, S., & Srivastava, J. (1996).

Working Paper. Twin Cities, MN: University of Minnesota, Twin Cities Campus.

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References

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdf

http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdf

http://www.geocities.com/anand_palm/

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Abstract

VDM refers to refers to the use of visualization techniques in Data Mining process to

Evaluate Monitor Guide

This paper provides a framework for VDM via the loose coupling of databases and visualization systems.

The paper applies VDM towards designing new algorithms that can learn decision trees by manually refining some of the decisions made by well known algorithms such as C4.5.

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Components of VQLBCI

The three major components of VQLBCI are Visual Representations, Computations and Events.

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Visual Development of Algorithms

Most interesting use of visual data mining is the development of new insights and algorithms.

The figure below shows the ER diagram for learning classification decision trees.

This model allows the user to monitor the quality and impact of decisions made by the learning procedure.

Learning procedure can be refined interactively via a visual interface.

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ER diagram for the search space of decision tree learning algorithm

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General Framework

Learning a classification decision tree from a training data set can be regarded as a process of searching for the best decision tree that meets user-provided goal constraints.

The problem space of this search process consists of Model Candidates, Model Candidate Generator and Model Constraints.

Many existing classification-learning algorithms like C4.5 and CDP fit nicely within this search framework. New learning algorithms that fit user’s requirements can be developed by defining the components of the problem space.

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General Framework

Model Candidate corresponds to the partial classification decision tree. Each node of the decision tree is a Model Atom

Search process is the process of finding a final model candidate such that it meets user goal specifications.

Model Candidate Generator transforms the current model candidate into a new model candidate by selecting one model atom to expand from the expandable leaf model atoms.

Model Constraints (used by Model Candidate Generator) provide controls and boundaries to the search space.

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Search Process

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Acceptability Constraint

Model Constraints consist of Acceptability constraints, Expandability constraints and a Data-Entropy calculation function.

Acceptability constraint predicate specifies when a model candidate is acceptable and thus allows search process to stop. EX:

A1) Total no of expandable leaf model atoms = 0. A2) Overall error rate of the model candidate <=

acceptable error rate. A3) Total number of model atoms in the model

candidate>= maximal allowable tree size.

A1 is used in C4.5 and CDP

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Expandability Constraint

An Expandability constraint predicate specifies whether a leaf model atom is expandable or not. EX: C4.5 uses E1 and E2 CDP uses E2 and E3

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Traversal Strategy

Traversal strategy ranks expandable leaf model atoms based on the model atom attributes. EX:

Increasing order of depth Decreasing order of depth Orders based on other model atom attributes.

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Steps in Visual Algorithm Development

No single algorithm is the best all the time, performance is highly data dependent.

By changing different predicates of model constraints, users can construct new classification-learning algorithm.

This enables users to find an algorithm that works the best on a given data set.

Two algorithms are developed : BF based on Best First search idea and CDP+ which is a modification of CDP

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BF

This algorithm is based on the Best-First search idea.

For Acceptability criteria, it includes A1 and A2 with a user specified acceptable error rate.

The Traversal strategy chosen is T3 In Best-First, expandable leaf model atoms

are ranked according to the decreasing order of the number of misclassified training cases. (local error rate * size of subset training data set)

The traversal strategy will expand a model atom that has the most misclassified training cases, thus reducing the overall error rate the most.

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CDP +

CDP+ is a modification of CDP

CDP has dynamic pruning using expandability constraint E3.

Here, the depth is modified according to the size of the training data set of the model atom.

We set B is the branching factor of the decision tree,

t is the size of training data set belonging to model atom, T is the whole training data set.

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Comparison of different classification learning algorithms

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Experiment

The new BF and CDP+ algorithms are compared with the C4.5 and CDP algorithms.

Various metrics are selected to compare the efficiency, accuracy and size of final decision trees of the classification algorithm.

The generation efficiency of the nodes is measured in terms of the total number of nodes generated.

To compare accuracy of the various algorithms, the mean classification error on the test data sets have been computed.

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Classification error for 10 data sets

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Nodes generated for 10 data sets

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Final decision tree size

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Results/Conclusion

CDP has accuracy comparable to C4.5 while generating considerably fewer nodes.

CDP+ has accuracy comparable to C4.5 while generating considerably fewer nodes.

CDP+ outperformed CDP in error rate and number of nodes generated.

Considering all performance metrics together, CDP+ is the best overall algorithm.

Considering classification accuracy alone, C4.5P is the winner.

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Conclusion

Different datasets require different algorithms for best results.

Diverse user requirements put different constraints on the final decision tree.

The experiment shows that Interactive Visual Data Mining Framework can help find the most suitable algorithm for a given data set and group of user requirements.

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Data Mining for Selective Visualization of Large Spatial

Datasets

Proceedings of 14th IEEE International Conference on Tools with Artificial Intelligence

(ICTAI'02), 2002. Washington (November 2002), DC, USA,

Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang, Rulin Liu

Computer Science & Engineering DepartmentUniversity of Minnesota

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References


















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Basic Terminology

Spatial databases Alphanumeric data + geographical cordinates

Spatial mining Mining of spatial databases

Spatial datawarehouse Contains geographical data

Spatial outliers Observations that appear to be inconsistent with

the remainder of that set of data

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Spatial Cluster

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Contribution

Propose and implement the CubeView visualization system

General data cube operations Built on the concept of spatial data

warehouse to support data mining and data visualization

Efficient and scalable spatial outlier detection algorithms

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Challenges in spatial data mining

Classical data mining - numbers and categories. Spatial data – more complex and extended objects such as points, lines and

polygons.

Second, classical data mining works with explicit inputs, whereas spatial predicates and attributes are often implicit.

Third, classical data mining treats each input independently of other inputs.

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Application Domain

The Traffic Management Center - Minnesota Department of Transportation (MNDOT) has a database to archive sensor network.

Sensor network includes about nine hundred stations each of which contains one to four loop detector

Measurement of Volume and occupancy. Volume is # vehicles passing through station in

5-minute interval Occupancy is percentage of time station is

occupied with vehicles

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Basic Concepts

Spatial Data Warehouse Spatial Data Mining Spatial Outliers Detection

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Spatial Data Warehouse

Employs data cube structure Outputs - albums of maps. Traffic data warehouse

Measures - volume and occupancy Dimensions - time and space.

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Spatial Data Mining

Process of discovering interesting and useful but implicit spatial patterns.

key goal is to partially ‘automate’ knowledge discovery

Search for “nuggets” of information embedded in very large quantities of spatial data.

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Spatial Outliers Detection

Suspiciously deviating observations Local instability Each Station

Spatial attributes – time, space Non spatial attributes – volume, occupancy

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Basic Structure – CubeView

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CubeView Visualization System

Each node in cube – a visualization style S - Traffic volume of station at all times. TTD – Time of the day TDW – Day of the week STTD – Daily traffic volume of each station TTD TDWS– Traffic volume at each station at

different times on different days

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Dimension Lattice

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Data Mining Algorithms for Visualization

Problem Definition

Given a spatial graph G ={ S , E } S - s1, s2, s3, s4…….. E – edges (neighborhood of stations) f ( x ) - attribute value for a data record N ( x )- fixed cardinality set of neighbors of x ) - Average attribute value of x

neighbors S( x ) - difference of the attribute value of each

data object and the average attribute value of neighbors.

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Problem Definition cont…

S( x ) - difference of the attribute value of each data object and the average attribute value of neighbors.

Test for detecting an outlier

confidence level threshold θ

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Few points

First, the neighborhood can be selected based on a fixed cardinality or a fixed graph distance or a fixed Euclidean distance.

Second, the choice of neighborhood aggregate function can be mean, variance, or auto-correlation.

Third, the choice for comparing a location with its neighbors can be either just a number or a vector of attribute values.

Finally, the statistic for the base distribution can be selected as normal distribution.

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Algorithms

Test Parameters Computation(TPC) Algorithm

Route Outlier Detection(ROD) Algorithm

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Software

http://www.cs.umn.edu/research/shashi-group/vis/traffic_volumemap2.htm

http://www.cs.umn.edu/research/shashi-group/vis/DataCube.htm

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Visualization and Data Mining techniques

Thank you!!!!

Documents

1 Visualization and Data Mining techniques By- Group number- 14 Chidroop Madhavarapu(105644921) Deepanshu Sandhuria(105595184) Data Mining CSE 634 Prof