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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 OntologiesSachin Singh, Pravin Vajirkar, and Yugyung LeeUniversity of Missouri–Kansas City.

Himani Tidke

CS 586

data mining
Data mining
  • Discovers useful interesting information
  • From large collections of data
  • Widely used as an active decision making tool.
  • Eg: Link Discoveries, WALMART
real world applications of data mining
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.
context based data mining
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.
context aware data mining framework
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.
  • 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.
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.
types of context
Types of Context
  • Domain Context- Target (Patient) Context
  • Location context
  • Data Context-combination of datasets
  • User Context-
    • User Identity Context
    • User History Context
example of context aware data mining
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.

  • 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.

context aware data mining framework11
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}.

the model
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
the model13
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


Thank You