ROLE OF ROUGH SET THEORY IN ROLE OF ROUGH SET THEORY IN CUSTOMER NEED IDENTIFICATION IN CUSTOMER NEED IDENTIFICATION IN

CONTEXT-AWARE COMPUTINGCONTEXT-AWARE COMPUTING

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INTRODUCTIONINTRODUCTION

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

MOTIVATIONMOTIVATION• Customer needs identificationCustomer needs identification

•Why is it important?Why is it important?• If needs are identified in a right manner before/during If needs are identified in a right manner before/during

developing a product or providing a service to developing a product or providing a service to customers, it makes easy to design a product/service customers, it makes easy to design a product/service satisfying customers in a communitysatisfying customers in a community

• Needs identification is the very first step in terms of Needs identification is the very first step in terms of both product design and service designboth product design and service design

• Currently, few research have been developed with Currently, few research have been developed with respect to needs identificationrespect to needs identification

OBJECTIVESOBJECTIVES• 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 context Incompleteness of context

• Extract rules to classify needs from huge context as Extract rules to classify needs from huge context as datadata

• 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

CUSTOMER NEEDS CUSTOMER NEEDS IDENTIFICATIONIDENTIFICATION

• ‘‘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 be’A gap between what ‘is’ and what ‘should be’• Researchers’ definitionsResearchers’ definitions

• Need as a gap between actual and ideal identified as community value Need as a gap between actual and ideal identified as community value • Wants or a demand Wants or a demand

• Need identificationNeed identification• Originated from recognizing unfulfilled needsOriginated from recognizing unfulfilled needs• Consumer Buying Behavior model in management Consumer 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 customer’s purchase behaviorRelated to customer’s 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

CONTEXT-AWARE COMPUTINGCONTEXT-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 User context – any information characterizing the situation of an

entityentity• Location of userLocation of user• Collection of nearby people and objectsCollection of nearby people and objects• Accessible devices, and Accessible 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 It makes computer technology melt and transparently weave into

our livesour 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

NEED IDENTIFICATION & NEED IDENTIFICATION & CONTEXT-AWARECONTEXT-AWARE

Human need identificationHuman need identification Not supported by computer Not supported by computer

systems systems Legacy need identificationLegacy need identification

No stimulus, no No stimulus, no identificationidentification

Context provided manuallyContext provided manually Context-aware need Context-aware need

identificationidentification(need awareness)(need awareness)

MACHINE LEARNINGMACHINE LEARNING

• Rule based system (RBS)Rule based system (RBS)• Categorize context to specify user’s preferencesCategorize context to specify user’s preferences

• Machine learningMachine learning• E.g. Information filtering E.g. Information filtering

• DrawbacksDrawbacks• Rules required from domain expertRules required from domain expert• Complicated and time-consuming to write and maintain Complicated 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 applications Quickly 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

ROUGH SET THEORYROUGH SET THEORY

• Mathematical tool to deal with vague concepts for Mathematical tool to deal with vague concepts for representing ambiguity, vagueness and general representing ambiguity, vagueness and general uncertainty uncertainty • Algebraic properties of rough sets Different algebraic semantics Algebraic 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 Data mining, knowledge discovery, decision support, pattern

classification, and approximate reasoningclassification, and approximate reasoning

REDUCTS IN INFORMATIONREDUCTS IN INFORMATION & DECISION SYSTEM & DECISION SYSTEM

ReductReduct To reduce information (decision) systems by removing redundant To reduce information (decision) systems by removing redundant

attributesattributes Core. A minimal set of attributes from Core. A minimal set of attributes from AA, the set of all attributes, that , the set of all attributes, that

preserves the original classification defined by preserves the original classification defined by AA..

ATTRIBUTE ATTRIBUTE SELECTIONSELECTION

error theisgreater the1, tois Bεcloser the

error no causes Bin attributes of removal :0 where,0,1Bσ

dA,γ

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dB,AγdA,γBσ

A.B that Assume

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dA,U,A abledecision t ain a attributean of ceSignifican

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VALUE SET REDUCTIONVALUE SET REDUCTION• Discretization, used for real value attributesDiscretization, used for real value attributes

.cxa iff1,k

,1k,1,ifor ,c,cxa iffi,

,cxa iff0,

xaAa:aA

CC

ccc where,ca,,,ca,,ca,C

d,AU,ALet

A oftion discretiza-C

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Aon cuts basic ofset The

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MINIMAL DECISIONMINIMAL DECISION RULES RULES

Construct a decision-relative discernibility function Construct a decision-relative discernibility function ffxxrr

by considering the row corresponding to object by considering the row corresponding to object xx in the in the decision-relative discernibility matrix for A.decision-relative discernibility matrix for A.

Compute all prime implicants of Compute all prime implicants of ffxxrr..

On the basis of the prime implicants, create minimal On the basis of the prime implicants, create minimal rules corresponding to rules corresponding to xx. To do this, consider the set . To do this, consider the set AA((II) of attributes corresponding to propositional ) of attributes corresponding to propositional variables in variables in II, for each prime implicant, for each prime implicant I I, and construct , and construct the rule:the rule:

xddxaa

IAa

CONCLUSIONCONCLUSION Rough Set Theory (RST) is very applicable to identify Rough Set Theory (RST) is very applicable to identify

needs in context-aware computing environment.needs in context-aware computing environment. RST can approximate incomplete context with RST can approximate incomplete context with

approximation rules.approximation rules. RST can extract rules from context and key attributes RST can extract rules from context and key attributes

with respect to needs by finding relationship between with respect to needs by finding relationship between contexts and needs.contexts and needs.

RST can deal with symbolic values as well as real RST can deal with symbolic values as well as real values.values.

FUTURE WORKSFUTURE WORKS Implementation of context-aware needs identification system Implementation of context-aware needs identification system

with Rough Set Theorywith Rough Set Theory Comparing the implementation with applications using CBR and Comparing the implementation with applications using CBR and

etc.etc.

REFERENCESREFERENCES• 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 Bazan, Jan; Nguyen, Hung Son and Szczuka, Marcin

(2004). "A view on rough set concept approximations". (2004). "A view on rough set concept approximations". Fundamenta InformaticaeFundamenta Informaticae 59: 107–118. 59: 107–118.

• Wong, S. K. M.; Ziarko, Wojciech and Ye, R. Li (1986). Wong, S. K. M.; Ziarko, Wojciech and Ye, R. Li (1986). "Comparison of rough-set and statistical methods in "Comparison of rough-set and statistical methods in inductive learning". inductive learning". International Journal of Man-International Journal of Man-Machine StudiesMachine Studies 24: 53–72.  24: 53–72. 

• http://www.gosephtechnologies.orghttp://www.gosephtechnologies.org• http://www.gorbachov.co.nrhttp://www.gorbachov.co.nr