Romi Dm 05 Research June2012

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Data Mining:Penelitian Data Mining

Romi Satria [email protected]://romisatriawahono.net

+6281586220090


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SD Sompok Semarang (1987)

SMPN 8Semarang (1990)

SMA Taruna Nusantara, Magelang (1993)

S1, S2 dan S3 (on-leave)

Department of Computer SciencesSaitama University, Japan (1994-2004)

Research Interests: Software EngineeringandIntelligent Systems

Founder IlmuKomputer.Com

Peneliti LIPI (2004-2007)

Founder dan CEO PT Brainmatics Cipta Informatika

Romi Satria Wahono


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Course Outline

1. Pengenalan Data Mining

2. Proses Data Mining

3. Evaluasi dan Validasi pada Data Mining

4. Metode dan Algoritma Data Mining

5. Penelitian Data Mining


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Penelitian Data Mining


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Penelitian Data Mining

1. Standard Proses Penelitian pada Data Mining

2. Journal Publications on Data Mining

3. Research on Classification

4. Research on Clustering

5. Research on Prediction

6. Research on Association Rule


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Standard Proses Penelitianpada Data Mining


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Data Mining Standard Process (CRISPDM)

A cross-industry standard was clearly required

that is industry neutral, tool-neutral, andapplication-neutral

The Cross-Industry Standard Process for Data

Mining (CRISP

DM) was developed in 1996(Chapman, 2000)

CRISP-DM provides a nonproprietary and freely

available standard process for fitting datamining into the general problem-solvingstrategy of a business or research unit


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CRISP-DM


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1. Business Understanding Phase

Enunciate the project objectives and requirements

clearly in terms of the business or research unit as awhole

Translate these goals and restrictions into the

formulation of a data mining problem definition Prepare a preliminary strategy for achieving these

objectives


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2. Data Understanding Phase

Collect the data

Use exploratory data analysis to familiarize yourselfwith the data and discover initial insights

Evaluatethe quality of the data

If desired, select interesting subsets that maycontain actionable patterns


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3. Data Preparation Phase

Prepare from the initial raw data the final data set

that is to be used for all subsequent phases. Thisphase is very labor intensive

Select the cases and variables you want to analyze

and that are appropriate for your analysis Perform transformations on certain variables, if

needed

Clean the raw data so that it is ready for themodeling tools


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4. Modeling phase

Select and apply appropriate modeling techniques

Calibrate model settingsto optimize results

Remember that often, several different techniquesmay be usedfor the same data mining problem

If necessary, loop back to the data preparationphaseto bring the form of the data into line withthe specific requirements of a particular data

mining technique


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5. Evaluation phase

Evaluate the one or more models delivered in the

modeling phase for quality and effectivenessbefore deploying them for use in the field

Determine whether the model in fact achieves the

objectives set for it in the first phase Establish whether some important facet of the

business or research problem has not beenaccounted for sufficiently

Come to a decision regarding use of the datamining results


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6. Deployment phase

Make use of the models created: Model creation

does not signify the completion of a project

Example of a simple deployment: Generate areport

Example of a more complex deployment:Implement a parallel data mining process inanother department

For businesses, the customer often carries out thedeployment based on your model


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Latihan

Pelajari dan pahami Case Study 1-5 dari buku

Larose (2005) Chapter 1

Pelajari dan pahami bagaimana menerapkanCRISP-DMpada tesis Firmansyah (2011) tentangpenerapan algoritma C4.5 untuk penentuankelayakan kredit


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Journal Publications on Data Mining


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Transactions and Journals

Review Paper (survey and state-of-the-art):

ACM Computing Surveys (CSUR)

Research Paper (technical):

ACM Transactions on Knowledge Discovery from Data (TKDD)

ACM Transactions on Information Systems (TOIS)

IEEE Transactions on Knowledge and Data Engineering

Springer Data Mining and Knowledge Discovery

International Journal of Business Intelligence and Data Mining(IJBIDM)


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Cognitive Assignment III

1. Baca 1 paper ilmiah yang diterbitkan di journal 2010-2012 yang

berhubungan dengan metode data mining yang sudah kitapelajari

2. Rangkumkan masing-masing dalam bentuk slide denganstruktur:

1. Latar Belakang Masalah (Research Background)2. Pernyataan Masalah (Problem Statements)

3. Pertanyaan Penelitian (Research Questions)

4. TujuanPenelitian (Research Objective)

5. Metode-Metode yang Sudah Ada(Existing Methods)6. Metode yang Diusulkan (Proposed Method)

7. Hasil (Results)

8. Kesimpulan(Conclusion)

3. Presentasikan di depan kelas pada mata kuliah berikutnya


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Referensi

1. Ian H. Witten, Frank Eibe, Mark A. Hall, Data mining: Practical

Machine Learning Tools and Techniques 3rd Edition, Elsevier,2011

2. Daniel T. Larose, Discovering Knowledge in Data: anIntroduction to Data Mining,John Wiley & Sons, 2005

3. Florin Gorunescu, Data Mining: Concepts, Models andTechniques, Springer, 2011

4. Jiawei Han and Micheline Kamber, Data Mining: Concepts andTechniques Second Edition, Elsevier, 2006

5. Oded Maimon and Lior Rokach, Data Mining and KnowledgeDiscovery Handbook Second Edition, Springer, 2010

6. Warren Liao and Evangelos Triantaphyllou (eds.), RecentAdvances in Data Mining of Enterprise Data: Algorithms andApplications, World Scientific, 2007

Documents

Romi Dm 05 Research June2012