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Context-based Data Mining Using Ontologies Sachin Singh, Pravin Vajirkar, and Yugyung Lee University of Missouri – Kansa

Context-based Data Mining Using Ontologies Sachin Singh, Pravin Vajirkar, and Yugyung Lee University of Missouri – Kansas City. Himani Tidke CS 586 Data mining Discovers useful interesting information From large collections of data Widely used as an active decision making tool.

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Context-based Data Mining Using Ontologies Sachin Singh, Pravin Vajirkar, and Yugyung Lee University of Missouri – Kansa

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  1. Context-based Data Mining Using OntologiesSachin Singh, Pravin Vajirkar, and Yugyung LeeUniversity of Missouri–Kansas City. Himani Tidke CS 586

  2. Data mining • Discovers useful interesting information • From large collections of data • Widely used as an active decision making tool. • Eg: Link Discoveries, WALMART

  3. Real-world applications of data mining • Require a dynamic and resilient model. • Wide variety of diverse and unpredictable contexts. • Huge amount of data . • Encompass entities that evolve over time. • Due to dynamic nature of environment, data must be interpreted differently depending upon situation (context). • For instance, the meaning of a cold patient’s high fever might be different from the fever of a pneumonia patient.

  4. Context Based Data Mining • Consist of circumstantial aspects of the user and domain that may affect the data mining process. • May improve performance and efficacy of data mining . • Context-aware data mining is related to how the attributes should be interpreted under specific request criteria.

  5. Context-aware Data Mining Framework • Context will be represented in an ontology. • Context will be automatically captured during data mining process. • Context will allow the adaptive behavior to carry over to powerful data mining.

  6. Ontologies • Represent information or knowledge that is machine processable and can be communicated between different agents. • We can differentiate the context aware data mining into two parts; • Actual representation of the context factor for a domain in a corresponding ontology. • A generic framework which can query this ontology and invoke the mining processes and coordinate them according to the ontology design.

  7. Context-Awareness • Information has to be conveyed from one element to another we need to let the receiving element know the reference of our discussion. • Such as location, environment, identity of people and time. • Determines an application behavior or describes where the event occurs. • Lack of context-awareness leads to missing a lot of critical and useful information that would affect the data mining process and results.

  8. Types of Context • Domain Context- Target (Patient) Context • Location context • Data Context-combination of datasets • User Context- • User Identity Context • User History Context

  9. Example of Context-Aware Data Mining • Doctor wants to know the likelihood of a patient having the major blood vessels < 50% or > 50% narrowing as a measure of heart attack risk. • Attributes of the dataset: Age, Sex, Symptoms of smoke disease , resting blood pressure, Serum cholesterol, Location of the person where he lives, Diagnosis of heart (angiographic disease status), etc.

  10. Contd. • Diagnosis of heart is the pivot element (class attribute)Rest are query parameters. • Location Context- not a significant node in the classification tree. It can be used to cluster data records based on that.Eg Zones, States. • Domain context(Patient context)- Historical patient repository. • Data Context-Smoke Disease The system picks up a dataset referring to Smoking Effects, mines it and builds the classification tree, which predicts if the person has smoking problems.

  11. Context-Aware Data Mining Framework • A set of context factors which may affect the behavior of data mining: C = {c1, c2,. . ., cn}. • Context factor takes values {c1, c2, . . ck}. • D a dataset composed of a set of tuples, T = {t1, t2, . . ., tn}, A , a set of attributes= {a1, a2, . . ., am}, V a set of values for a given attribute aj , {v1, v2, . . ., vl}.

  12. The Model • Phase 1.-Preprocessing. Datasets to be mined are prepared using different schemas against tuples (T), attributes (A) or values (V ) of available datasets (D): • Pick • Join • Trim

  13. The Model • Phase 2. Data Mining. Types of mining processes : • Cascading Mining Process • Sequential Mining Process • Iterative Mining Process • Parallel fork process • Aggregating Mining Process

  14. Architecture

  15. Ontology Design

  16. Questions? Thank You

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