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ROLE OF ROUGH SET THEORY IN CUSTOMER NEED IDENTIFICATION IN CONTEXT-AWARE COMPUTING IN ASSOCIATION WITH

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Presentation on theme: "ROLE OF ROUGH SET THEORY IN CUSTOMER NEED IDENTIFICATION IN CONTEXT-AWARE COMPUTING IN ASSOCIATION WITH"— Presentation transcript:

1 ROLE OF ROUGH SET THEORY IN CUSTOMER NEED IDENTIFICATION IN CONTEXT-AWARE COMPUTING IN ASSOCIATION WITH http://www.gorbachov.co.nr gorvachove@gmail.com (+91) 9937870838 Copy Right © 2008, by GORVACHOVE PRESENTS ORISSA INSTITUTE OF TECHNOLOGY, BURLA

2 INTRODUCTION A rough set is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set. The lower and upper approximation sets themselves are crisp sets in the standard version of rough set theory, but in other variations, the approximating sets may be fuzzy sets as well. A rough set is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set. The lower and upper approximation sets themselves are crisp sets in the standard version of rough set theory, but in other variations, the approximating sets may be fuzzy sets as well.

3 MOTIVATION Customer needs identificationCustomer needs identification Why is it important?Why is it important? If needs are identified in a right manner before/during developing a product or providing a service to customers, it makes easy to design a product/service satisfying customers in a communityIf needs are identified in a right manner before/during developing a product or providing a service to customers, it makes easy to design a product/service satisfying customers in a community Needs identification is the very first step in terms of both product design and service designNeeds identification is the very first step in terms of both product design and service design Currently, few research have been developed with respect to needs identificationCurrently, few research have been developed with respect to needs identification

4 OBJECTIVES Customer needs identification with contextCustomer needs identification with context Challenge of dynamic characteristic of contextChallenge of dynamic characteristic of context Relationship between contextsRelationship between contexts Incompleteness of contextIncompleteness of context Extract rules to classify needs from huge context as dataExtract rules to classify needs from huge context as data Extract key context with respect to needsExtract key context with respect to needs Value sets classification of context dataValue sets classification of context data Real (continuous) valuesReal (continuous) values Symbolic valuesSymbolic values Roles of Rough Set Theory in identifying needsRoles of Rough Set Theory in identifying needs

5 CUSTOMER NEEDS IDENTIFICATION Need is a sort of internal state to do somethingNeed is a sort of internal state to do something A gap between what is and what should beA gap between what is and what should be Researchers definitionsResearchers definitions Need as a gap between actual and ideal identified as community valueNeed as a gap between actual and ideal identified as community value Wants or a demandWants or a demand Need identificationNeed identification Originated from recognizing unfulfilled needsOriginated from recognizing unfulfilled needs Consumer Buying Behavior model in managementConsumer Buying Behavior model in management Focusing on buying behavior of customersFocusing on buying behavior of customers Needs as the first step of buying procedureNeeds as the first step of buying procedure Related to customers purchase behaviorRelated to customers purchase behavior It will affect product design and service design by fulfilling unmet needsIt will affect product design and service design by fulfilling unmet needs

6 CONTEXT-AWARE COMPUTING Context is a key to identify information related to customerContext is a key to identify information related to customer User context – any information characterizing the situation of an entityUser context – any information characterizing the situation of an entity Location of userLocation of user Collection of nearby people and objectsCollection of nearby people and objects Accessible devices, andAccessible devices, and Changes to objects over timeChanges to objects over time Context-aware computing technologyContext-aware computing technology It makes computer technology melt and transparently weave into our livesIt makes computer technology melt and transparently weave into our lives Context-aware computing and needs identificationContext-aware computing and needs identification Promising method to identify customer needsPromising method to identify customer needs Pattern recognition based on context related to customersPattern recognition based on context related to customers

7 NEED IDENTIFICATION & CONTEXT-AWARE Human need identification Human need identification Not supported by computer systems Not supported by computer systems Legacy need identification Legacy need identification No stimulus, no identification No stimulus, no identification Context provided manually Context provided manually Context-aware need identification (need awareness) Context-aware need identification (need awareness)

8 MACHINE LEARNING Rule based system (RBS)Rule based system (RBS) Categorize context to specify users preferencesCategorize context to specify users preferences Machine learningMachine learning E.g. Information filteringE.g. Information filtering DrawbacksDrawbacks Rules required from domain expertRules required from domain expert Complicated and time-consuming to write and maintainComplicated and time-consuming to write and maintain Inflexible with unspecified conditionsInflexible with unspecified conditions Case based reasoning (CBR)Case based reasoning (CBR) Quickly adopted in context-aware applicationsQuickly adopted in context-aware applications DrawbacksDrawbacks Similarity calculation limited to symbolic valuesSimilarity calculation limited to symbolic values Priority between casesPriority between cases Capability dealing with incomplete informationCapability dealing with incomplete information

9 ROUGH SET THEORY Mathematical tool to deal with vague concepts for representing ambiguity, vagueness and general uncertaintyMathematical tool to deal with vague concepts for representing ambiguity, vagueness and general uncertainty Algebraic properties of rough sets Different algebraic semanticsAlgebraic properties of rough sets Different algebraic semantics Focus on indiscernibility and reductsFocus on indiscernibility and reducts Combination approach with Boolean reasoningCombination approach with Boolean reasoning Adopted in various researchAdopted in various research Data mining, knowledge discovery, decision support, pattern classification, and approximate reasoningData mining, knowledge discovery, decision support, pattern classification, and approximate reasoning

10 REDUCTS IN INFORMATION & DECISION SYSTEM Reduct Reduct To reduce information (decision) systems by removing redundant attributes To reduce information (decision) systems by removing redundant attributes Core. A minimal set of attributes from A, the set of all attributes, that preserves the original classification defined by A. Core. A minimal set of attributes from A, the set of all attributes, that preserves the original classification defined by A.

11 ATTRIBUTE SELECTION

12 VALUE SET REDUCTION Discretization, used for real value attributesDiscretization, used for real value attributes

13 MINIMAL DECISION RULES Construct a decision-relative discernibility function f x r by considering the row corresponding to object x in the decision-relative discernibility matrix for A. Construct a decision-relative discernibility function f x r by considering the row corresponding to object x in the decision-relative discernibility matrix for A. Compute all prime implicants of f x r. Compute all prime implicants of f x r. On the basis of the prime implicants, create minimal rules corresponding to x. To do this, consider the set A(I) of attributes corresponding to propositional variables in I, for each prime implicant I, and construct the rule: On the basis of the prime implicants, create minimal rules corresponding to x. To do this, consider the set A(I) of attributes corresponding to propositional variables in I, for each prime implicant I, and construct the rule:

14 CONCLUSION Rough Set Theory (RST) is very applicable to identify needs in context-aware computing environment. Rough Set Theory (RST) is very applicable to identify needs in context-aware computing environment. RST can approximate incomplete context with approximation rules. RST can approximate incomplete context with approximation rules. RST can extract rules from context and key attributes with respect to needs by finding relationship between contexts and needs. RST can extract rules from context and key attributes with respect to needs by finding relationship between contexts and needs. RST can deal with symbolic values as well as real values. RST can deal with symbolic values as well as real values. FUTURE WORKS Implementation of context-aware needs identification system with Rough Set Theory Implementation of context-aware needs identification system with Rough Set Theory Comparing the implementation with applications using CBR and etc. Comparing the implementation with applications using CBR and etc.

15 REFERENCES http://en.wikipedia.org/wiki/Rough_sethttp://en.wikipedia.org/wiki/Rough_set http://www.google.comhttp://www.google.com Bazan, Jan; Nguyen, Hung Son and Szczuka, Marcin (2004). "A view on rough set concept approximations". Fundamenta Informaticae 59: 107–118.Bazan, Jan; Nguyen, Hung Son and Szczuka, Marcin (2004). "A view on rough set concept approximations". Fundamenta Informaticae 59: 107–118. Wong, S. K. M.; Ziarko, Wojciech and Ye, R. Li (1986). "Comparison of rough-set and statistical methods in inductive learning". International Journal of Man-Machine Studies 24: 53–72.Wong, S. K. M.; Ziarko, Wojciech and Ye, R. Li (1986). "Comparison of rough-set and statistical methods in inductive learning". International Journal of Man-Machine Studies 24: 53–72. http://www.gosephtechnologies.orghttp://www.gosephtechnologies.org http://www.gorbachov.co.nrhttp://www.gorbachov.co.nr

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