Download Data Preparation

國立雲林科技大學
National Yunlin University of Science and Technology
Exploiting data preparation to enhance
mining and knowledge discovery
Advisor：Dr.Hsu
Graduate： Keng-Wei Chang
Author： Balaji Rajagopalan
Mark W. Isken
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C:
APPLICATIONS AND REVIEWS, VOL. 31, NO. 4, NOVEMBER 2001
Intelligent Database Systems Lab
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Outline
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Motivation
Objective
Introduction
Data Preparation
Research Method
Results
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Motivation
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using organizational data for mining and
knowledge discovery
not amenable for mining in its natural form
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Objective
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data enhancement by the introduction of new
attributes along with judicious aggregation of
existing attributes
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results in higher quality knowledge discovery
differential impact on the performance of different
mining algorithms
Intelligent Database Systems Lab
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Introduction
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Exponential growth information result a
tremendous volume of data to knowledge
workers.
Knowledge management solution
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Knowledge repository
Knowledge sharing
Knowledge discovery
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Data Preparation
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Present a framework based on prior research in
knowledge discovery
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Data quality
Data characteristics
Data preparation
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Research Method
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data set from a large tertiary care hospital in
the United States was used
few topics
A. Problem Domain
B. Data
C. Clustering Algorithms for Knowledge Discovery
D. Entropy-Based Metrics for Cluster Quality
Assessment
E. Rule Extraction Metrics
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Problem Domain
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allocation of inpatient beds
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more difficult is use quantitative resource
allocation in a manageable set of patient types
quantitative resource
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sequence of hospital units visited and corresponding
length of stay
patient types
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a group of patients consuming a similar level of hospital
resources
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Problem Domain
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refer to this as the patient classification
problem
too few V.S. too many patient types
The key is identify the set of patient types
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Data
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Inpatient obstetrical and gynecological
(OB/GYN) patient flow
There are numerous fields
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demographics
physician information
ICD9-CM diagnostic
procedure codes
diagnosis-related groups (DRGs)
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Data
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almost 500 defined in DRGs
range[353-384] are related to OB/GYN
grouping these DRGs into five DRG types
Intelligent Database Systems Lab
Clustering Algorithms for Knowledge
Discovery
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K-means and Kohonen seof-organizing
Similarity
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Euclidean distance function
d x, y  
n
 x
i 1
i
 yi 
2
Intelligent Database Systems Lab
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Entropy-Based Metrics for Cluster
Quality Assessment
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Entropy
 1 
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E j   pij log 2 
p 
i
 ij 
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nijbe the number of cases having a
DRG type of i in cluster j
pij  nij / l nlj
Weighted Entropy
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N.Y.U.S.T.
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cluster size
calculate a weighted average entropy measure for
a cluster solution
Purity, let
Pj  max i pij
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Rule Extraction Metrics
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expect a high degree of resonance for most of
the rules with our domain knowledge
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Results
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detail the data enhancements relevant to this
study
A. Data Preparation : Basics
B. Mining and Knowledge Discovery
C. Differential Impact Based on Clustering Method
D. Usefulness of Knowledge Discovered
E. Limitations
F. Implications for Research and Practice
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Data Preparation : Basics
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Data set included fields that represent the path
and associated lengths of stay along that path
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Data Preparation : Basics
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Consider three data sets characterized in order
to illustrate the impact of data preparation
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ED1
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Eight numeric variables
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Data Preparation : Basics
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ED2
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Both DRG and CCS were designed to serve as
aggregate measures of hospital resource
consumption
in addition ED1, ED2 add five nominal variables
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Data Preparation : Basics
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ED3
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in addition to ED2, ED3 contains two binary
variables
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whether or not gave birth during the visit
whether or not gave birth via C-section
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Mining and Knowledge Discovery
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Mining and Knowledge Discovery
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Differential Impact Based on Clustering
Method
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Usefulness of Knowledge Discovered
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Limitations
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may not exactly applicable in every case
examine only two data mining algorithms
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K-means and Kohonen self-organizing maps
illustrative, not exhaustive
domain knowledge played a critical role in the
data preparation process
Intelligent Database Systems Lab
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Implications for Research and Practice
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provides empirical evidence demonstrating the
impact of data preparation on mining and
knowledge discovery
engage in a comparative investigation of
multiple altorithms
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Personal opinion
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…
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