This document discusses different methods for automatic classification of documents. It describes supervised classification where a training set of known sample objects is used to determine how to weight parameters and group objects into classes. The document also discusses different types of attributes, relationships between classes and properties, and measures of association that can be used for classification. Finally, it introduces probabilistic indexing which uses probability to measure relevance of documents to a given topic.

2. Automatic Classification
 Classification???
 Classificatory systems
 Output of such system
 Example of classification :
 Indexing
 Classification v/s Diagnosis ??
 Classification = grouping
 Diagnosis = identification

3. Classification Methods
 Classification Methods
 Why??
 Data
 Objects
 Documents , keywords, characters
 Data & objects
 Corresponding description
 attributes

4. Classification 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
 ??
 ??

5. Classification 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

6. Classification 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

7. Classification Methods
 Binary State
 Bold , underline
 Multi State
 Color , position , font type
 Execution of operation changes attribute value.
 Example:
 MOVE
 FILL
 INSERT
 DELETE
 CREATE

8. Classification Methods
 Relation between Classes & Properties
1. 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

9. Classification Methods
 Relation between Object & Classes
1. Exclusive:
 Object belongs to single class
 Example
1. Overlapping:
 Membership is with different classes
 Example

10. Classification Methods
 Relationship between Classes & Classes:
1. Ordered:
 Structure is imposed
 Hierarchical structure
 Example
1. Unordered:
 No imposed structure
 All are at same level
 example

11. Measures 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

12. Measures 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.

13. Measures of Association
 Association:
 Association means???
 Dependency…
 Occurrence…
 reserved for the similarity between objects
characterized by discrete-state attributes.

14. Measures 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

15. Measures of Association
 Used in information and data retrieval
 | | specifies size of set

16. Probabilistic 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

17. Probabilistic 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.