data mining in knowledge management

Data Mining in Knowledge Data Mining in Knowledge ManagementManagement

Fakulti Sains Komputer & Fakulti Sains Komputer & Teknologi MaklumatTeknologi Maklumat

Fatimah SidiFatimah Sidi

19/06/200219/06/2002

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Definitions of KM

■ Address business Address business problems particular problems particular to businessto business– creates and deliver innovative products or creates and deliver innovative products or

services; services; – managing and enhancing relationships with managing and enhancing relationships with

existing and and new customers, partners, existing and and new customers, partners, and suppliers; or and suppliers; or

– administering and improving work practices administering and improving work practices and processes. (Tiwana, 2000)and processes. (Tiwana, 2000)

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Definitions of KM

■ A system produces knowledgeA system produces knowledge – gathers information gathers information – compares conceptual formulations compares conceptual formulations

describing and evaluating its experience, describing and evaluating its experience, with its goals, objectives, expectations or with its goals, objectives, expectations or past formulations of descriptions, or past formulations of descriptions, or evaluations by comparison with reference evaluations by comparison with reference to validation criteria (Firestone, 1998)to validation criteria (Firestone, 1998)

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Definitions of KM

■ A system maintains knowledge by A system maintains knowledge by continues to evaluate its knowledge continues to evaluate its knowledge base against new information by base against new information by subjecting the knowledge base to subjecting the knowledge base to continuous testing against its validation continuous testing against its validation criteria.criteria.

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Definitions of KM

■ requires a knowledge base to begin requires a knowledge base to begin operation where it enhances its own operation where it enhances its own knowledge base with the passage of knowledge base with the passage of time because it is a self-correcting time because it is a self-correcting system, and subjects its knowledge system, and subjects its knowledge base to testing against experience.base to testing against experience.

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Definitions of KM

■ ““re-badging” of earlier information and re-badging” of earlier information and data management methodsdata management methods

■ Like any system of thgought that has Like any system of thgought that has value, both old and new and its value, both old and new and its combined new ideas with ideas that combined new ideas with ideas that “everyone has know all along” (Prusak, “everyone has know all along” (Prusak, 2001)2001)

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Definitions of KM

■ ConclusionConclusion– Knowledge Management is Knowledge Management is

providing the growth of knowledge providing the growth of knowledge and also a new ways to channel and also a new ways to channel raw data into meaningful raw data into meaningful information which in turn can information which in turn can become knowledgebecome knowledge

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Difference Between Data, Information & Knowledge

■ DataData– facts, numbers, or text facts, numbers, or text – operational or transactional dataoperational or transactional data– non operational datanon operational data– metadata - data about the data metadata - data about the data

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■ InformationInformation– Collection of data is not information Collection of data is not information

unless exist relation between the dataunless exist relation between the data– Patterns, associations or relationships Patterns, associations or relationships

among data provide informationamong data provide information

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■ KnowledgeKnowledge– Information converted to knowledge Information converted to knowledge

about historical patterns and future about historical patterns and future trendstrends

– Subset of information Subset of information extracted, filtered or formatted in a very extracted, filtered or formatted in a very

special wayspecial way Subjected to and passed tests of validationSubjected to and passed tests of validation

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■ KnowledgeKnowledge

Common sense knowledge is Common sense knowledge is information that has been validated by information that has been validated by common sense experiencecommon sense experience

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Scientific knowledge is information Scientific knowledge is information (hypotheses and theories) validated (hypotheses and theories) validated by rules and tests applied to it by by rules and tests applied to it by some scientific communitysome scientific community

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Organizational knowledge is Organizational knowledge is information validated by rules and information validated by rules and tests of the organization seeking tests of the organization seeking knowledge that improves knowledge that improves organizational performanceorganizational performance

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leads to Wisdom arises when one leads to Wisdom arises when one understands the foundational understands the foundational principles responsible for the patterns principles responsible for the patterns representing knowledge.representing knowledge.

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data understanding

information

Understandingrelations

knowledge

Understandingpatterns

wisdom

Understandingprinciples

Contextindependece

( Gene Bellinger)( Gene Bellinger)

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Components KM technology framework (Tiwana, 2000)

Knowledge Management

Workflow

Data Mining

Project Management

Document Management

Groupware

Decision Support System

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Components KM technology framework (Tiwana, 2000)

■ Key Functions :-Key Functions :-– Knowledge FlowKnowledge Flow– Information mappingInformation mapping– Information sourcesInformation sources– Information and knowledge exchangeInformation and knowledge exchange– Intelligent agent and network miningIntelligent agent and network mining

Finding knowledgeFinding knowledge

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Data mining in KM

■ mechanism to appropriately cluster mechanism to appropriately cluster search results in different pre-specified search results in different pre-specified content categories as specified in the content categories as specified in the knowledge map. knowledge map.

■ Drill down into a relevant category Drill down into a relevant category without having to learn the subtleties of without having to learn the subtleties of complex query languages and syntaxescomplex query languages and syntaxes

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Definitions of DM

■ Sometimes called data or knowledge Sometimes called data or knowledge discovery discovery

■ Process Process of analyzing data from different of analyzing data from different perspectives and summarizing it into perspectives and summarizing it into useful information anduseful information and

■ Finding correlations or patterns among Finding correlations or patterns among dozens of fields in large relational dozens of fields in large relational databases. databases.

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Definitions of DM

■ (Holsheimer and Siebes, 1994) (Holsheimer and Siebes, 1994) – searching for relationships and global searching for relationships and global

patterns that exist in large databases, but patterns that exist in large databases, but are “hidden” among the vast amounts of are “hidden” among the vast amounts of data. data.

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Definitions of DM

■ (Miller and Rohberg, 1996) (Miller and Rohberg, 1996) – tool that identifies and characterize tool that identifies and characterize

interrelationships among multivariable interrelationships among multivariable dimensions without requiring a human to dimensions without requiring a human to ask specific questions. ask specific questions.

– looks for trends and patterns looks for trends and patterns – finds relationships and make prediction.finds relationships and make prediction.

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Definitions of DM

■ (Han and Kamber, 2001)(Han and Kamber, 2001)– extracting or “mining” knowledge from extracting or “mining” knowledge from

large amounts of data. large amounts of data. – essential step in the process of knowledge essential step in the process of knowledge

discovery in databases, consists of an discovery in databases, consists of an iterative sequence of the following steps:iterative sequence of the following steps:

■ Data cleaningData cleaning

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Definitions of DM

– Data integrationData integration– Data selectionData selection– Data transformationData transformation– Data miningData mining– Pattern evaluationPattern evaluation– Knowledge presentationKnowledge presentation

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How does DM work?

■ Large scale information evolved Large scale information evolved transaction and analylitical systems transaction and analylitical systems separatelyseparately

■ DM provides link between the twoDM provides link between the two– Analyzes relationships and pattern in Analyzes relationships and pattern in

stored transaction data based on open stored transaction data based on open queries.queries.

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How does DM work?

■ Several types of analytical software Several types of analytical software available available – StatisticalStatistical– Machine learning andMachine learning and– Neutral networksNeutral networks

■ DM functionalities used to specify kind DM functionalities used to specify kind of pattern found in data mining task :of pattern found in data mining task :

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Classification of DM

■ Summarization Summarization (Holsheimer and Siebes, 1994)(Holsheimer and Siebes, 1994) ■ Association RulesAssociation Rules■ ClassificationClassification■ ClusteringClustering■ PredictionPrediction■ Sequential PatternsSequential Patterns■ Similarity SearchSimilarity Search

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Classification of DM

■ Similarity SearchSimilarity Search (Algawal & Swami, 1993)(Algawal & Swami, 1993) ■ Outlier Anlysis Outlier Anlysis (Han & Kamber, 2001)(Han & Kamber, 2001) ■ Evolution AnalysisEvolution Analysis

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Major element in DM

■ Extract, transform and load Extract, transform and load transactional data to DWtransactional data to DW

■ Store and manage the data Store and manage the data ■ Provide data access to business Provide data access to business

analysts and information technology analysts and information technology professionalsprofessionals

■ Analyze the dataAnalyze the data■ Present the dataPresent the data

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Levels of Analysis

■ Artificial neural networks: Non-linear Artificial neural networks: Non-linear predictive modelspredictive models

■ Genetic algorithmsGenetic algorithms■ Decision treesDecision trees■ Nearest neighbor methodNearest neighbor method■ Rule inductionRule induction■ Data visualizationData visualization

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Objectives of the study

■ To study the effective method of mining To study the effective method of mining the knowledge in data miningthe knowledge in data mining

■ To develop and implement the methods To develop and implement the methods in mining the knowledgein mining the knowledge

■ To test and measure its performance To test and measure its performance retrieving the knowledgeretrieving the knowledge

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Thank You

data mining in knowledge management

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