classification and association unit 1 IR

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.