Automatic ClassificationAutomatic Classification

Classification??? Classificatory systems Output of such system Example of classification :

Indexing

Classification v/s Diagnosis ?? Classification = grouping Diagnosis = identification

Classification MethodsClassification Methods

Classification Methods Why?? Data Objects

Documents , keywords, characters

Data & objects Corresponding description

attributes

Classification MethodsClassification Methods

Uses set of parameters to characterize each object Features should be relevant to task at hand Supervised classification

What classes??? Set of sample objects with known classes

Training set Set of known objects Used by classification program

Two phases for classification ?? ??

Classification MethodsClassification Methods

1. Training Phase: Uses training set Decision is about

How to weight parameters How to combine these objects under different classes

1. Application Phase: Weights determined in phase 1 are used with set of objects That do not have known classes Determine their possible class

Classification MethodsClassification Methods

With few parameters ; process is easy Example:

With much more parameters ; process is tough Example:

Depending on structure ; find types of attributes Multi State Attribute

Example:

Binary State Attribute Example:

Numerical Attributes Example

Classification MethodsClassification Methods

Binary State Bold , underline

Multi State Color , position , font type

Execution of operation changes attribute value. Example:

MOVE FILL INSERT DELETE CREATE

Classification MethodsClassification Methods

Relation between Classes & Properties1. Monothetic:

To get membership of class , object must posses the set of properties which are necessary as well as sufficient Example

1. Polythetic: Large number of members have some number of

properties No individual is having all the properties example

Classification MethodsClassification Methods

Relation between Object & Classes1. Exclusive:

Object belongs to single class Example

1. Overlapping: Membership is with different classes Example

Classification MethodsClassification Methods

Relationship between Classes & Classes:1. Ordered:

Structure is imposed Hierarchical structure Example

1. Unordered: No imposed structure All are at same level example

Measures of AssociationMeasures of Association

Some classification methods are based on a binary relationship between objects

On the basis of this relationship a classification method can construct a system of clusters

Relationship type:1. similarity

2. dissimilarity

3. association

Measures of AssociationMeasures of Association

Similarity: The measure of similarity is designed to quantify the likeness

between objects so that if one assumes it is possible to group objects in such a

way that an object in a group is more like the other members of the group

than it is like any object outside the group, then a cluster method enables such a group structure to be

discovered.

Measures of AssociationMeasures of Association

Association: Association means??? Dependency… Occurrence… reserved for the similarity between objects

characterized by discrete-state attributes.

Measures of AssociationMeasures of Association

Used to measure strength of relationship measure of association increases as the number or

proportion of shared attribute states increases. Five measures of association

1. Simple

2. Dice’s coefficient

3. Saccard’s coefficient

4. Cosine coefficient

5. Overlap coefficient

Measures of AssociationMeasures of Association

Used in information and data retrieval | | specifies size of set

Probabilistic IndexingProbabilistic Indexing

Probability of relevance Experiments and observations Sample space May Consist relevant as well as non relevant objects Consider a document Find no. of relevant document with respect to it That gives probability quotient probability measured as per the terms present in

document

Probabilistic IndexingProbabilistic Indexing

Probabilistic indexing model Contains random variable Denotes no. of relevant documents If this variable is selected by system Gives possible relevant document description Probabilistic information retrieval models are based on the

probabilistic ranking principle, which says that documents should be ranked according to

their probability of relevance with respect to the actual request.