Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Mining Favorable Facets

Presenter : Wei-Hao Huang 　Authors : Raymond Chi-Wing Wong, Jian Pei, Ada Wai-Chee Fu, Ke Wang

SIGKDD, 2008

Intelligent Database Systems Lab

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Outlines Motivation Objectives Methodology Experiments Conclusions Comments

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Motivation The importance of dominance and skyline

analysis in multi-criteria decision making applications.

Fixed order v.s. different customers may have different preferences on nominal attributes.

Finding favorable facets.

Intelligent Database Systems Lab

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I. M.Objectives

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Propose to minimal disqualifying condition (MDC) which can summarize favorable facets and is meaningful to the user.

Develop two algorithms:─ Computing MDC On-the-fly (MDC-O)─ A Materialization Method (MDC-M)

Use real data sets and synthetic data set to verify effectiveness and efficiency

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Methodology

Skyline analysis

Naïve Method

Minimal Disqualifying Conditions(MDC)

MDC On-the-fly (MDC-O)

A Materialization Method (MDC-M)

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Skyline analysis

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Naïve Method: Lattice Search

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Minimal Disqualifying Conditions

Used to summarize favorable facets effectively.

R’={(T,M)}R’’={(H,M)}MDC(f)={(T,M),(H,M)}

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MDC-O: Computing MDC On-the-fly

Point: P Data Set: DTemplate:

R

Process

MDC(P)

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MDC-M: A Materialization Method

Data Set: DTemplate: R

Process

SKY(R)MDC

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Indexing for Speed-up Use R-tree index structure An R-tree can be built the totally ordered

attributes T Find points that quasi-dominates p, a range

search is conducted on the R-tree

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Intelligent Database Systems Lab

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I. M.Experiments Synthetic Data Set

Dimension Numeric attributes Nominal attributes

Tuples Template Size Cardinality of Nominal Attributes Zipfian Parameter

Real Data Set Nursery Automobile

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I. M.Synthetic Data Set-Dimension(numeric attributes)

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Numeric 3 3 3 3

Nominal 1 2 3 4

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I. M.Synthetic Data Set-Dimension(nominal attributes)

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Numeric 2 3 4 5Nominal 1 1 1 1

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I. M.Synthetic Data Set-Tuples

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500k -> 1000k

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I. M.Synthetic Data Set-Template Size

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I. M.Synthetic Data Set-Cardinality of Nominal Attributes

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I. M.Real Data Set

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Nursery Data Set There are 12,960 instances and 8 attributes. The results in the performance are similar to synthetic data

sets.

Automobile Data Set Computation times were negligibly small. Honda, Mitsubishi and Toyota.Car Brand names MDCHonda Toyota <Honda

Mitsubishi Honda<Mitsubishi or Toyota < Mitsubishi

Toyota none

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Conclusions MDC is effective in summarizing the favorable

facets. The experimental results show proposed

methods are efficacious. Future work is used to dynamic data and

ordering is an interesting topic.

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Comments Advantages

─ Finding favorable facets which has not been studied before.

─ Effectiveness and the efficiency of the mining. Applications

─ Information retrieval