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Ir classification association

classification and association unit 1 IR

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Ir classification association

  1. 1. Automatic Classification Classification??? Classificatory systems Output of such system Example of classification :  Indexing Classification v/s Diagnosis ??  Classification = grouping  Diagnosis = identification
  2. 2. Classification Methods Classification Methods  Why??  Data  Objects  Documents , keywords, characters  Data & objects  Corresponding description  attributes
  3. 3. 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  ??  ??
  4. 4. Classification Methods1. Training Phase:  Uses training set  Decision is about  How to weight parameters  How to combine these objects under different classes1. Application Phase:  Weights determined in phase 1 are used with set of objects  That do not have known classes  Determine their possible class
  5. 5. 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
  6. 6. Classification Methods Binary State  Bold , underline Multi State  Color , position , font type Execution of operation changes attribute value. Example:  MOVE  FILL  INSERT  DELETE  CREATE
  7. 7. 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
  8. 8. Classification Methods Relation between Object & Classes 1. Exclusive:  Object belongs to single class  Example 1. Overlapping:  Membership is with different classes  Example
  9. 9. 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
  10. 10. 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
  11. 11. 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.
  12. 12. Measures of Association Association:  Association means???  Dependency…  Occurrence…  reserved for the similarity between objects characterized by discrete-state attributes.
  13. 13. 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
  14. 14. Measures of Association Used in information and data retrieval | | specifies size of set
  15. 15. 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
  16. 16. 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.