Modul Data Warehouse dan Data Mining
Download Modul Data Warehouse dan Data Mining Bab 14 - Implementasi Data Mining
Bab 14 - Implementasi Data Mining
Abstract
Menjelaskan implementasi data mining dalam aplikasi kasus
Kompetensi
Mahasiswa mampu memahami aplikasi data mining
Aplikasi Data Mining
• Data mining adalah disiplin ilmu yang masih baru dengan aplikasi yang luas dan
beragam
– Masih ada satu nontrivial gap antara prinsip umum dari data mining dan domainspecific,
effective data mining tools untuk aplikasi tertentu.
• Beberarap domain aplikasi, antara lain:
– Biomedical and DNA data analysis
– Financial data analysis
– Retail industry
– Telecommunication industry
Biomedical and DNA Data Analysis
• Urutan DNA: 4 blok dasar yang membangun DNA: (nucleotides): adenine (A), cytosine
(C), guanine (G), and thymine (T).
• Gene: satu urutan/barisan dari ratusan individual nucleotides tersusun dalam urutan
tertentu.
• Manusia mempunyai sekitar 30,000 genes
• Sangat banyak cara sehingga nucleotides dapat diurutkan dan dibariskan untuk
membentuk genes yang berbeda.
• Integrasi semantik dari keberagaman, database genome yang terdistribusi
– Current: highly distributed, uncontrolled generation dan menggunakan data DNA
yang sangat luas kebergamannya
– Metode Data cleaning dan data integration dikembangkan dalam data mining
akan membantu
Contoh Kasus Analisis DNA
• Pencarian keserupaan dan perbandingan diantara barisan DNA
– Bandingkan pola yang sering muncul dari setiap kelas (misal, penyakit dan
kesehatan)
– Identifikasi pola barisan gene yang berpengaruh dalam berbagai penyakit.
• Analisis Association : Pengidentifikasian dari kemunculan barisan gen
– Sebagian penyakit tidak di triger melalui satu gen tunggal tetapi oleh kombinasi
gen yang berlaku bersama.
– Analysis Association dapat membantu menentukan macam macam dari gen
yang kelihatannya akan muncul secara bersamaan dalam contoh target.
• Analisis Path : menghubungkan gen ke tingkatan pengembangan penyakit yang
berbeda.
– Gen yang berbeda dapat menjadi aktif pada tingkatan berbeda dari penyakit
– Mengembangkan intervensi pharmaceutical yang mentargetkan tingkatan yang
berbeda secara terpisah.
• Tool Visualisasi dan analisis data genetika
Data Mining untuk Analisis Data Keuangan
• Data keuangan terkumpul di bank dan intstitusi keuangan yang pada umumnya adalah
lengkap, handal dan tinggi kualitasnya.
• Desain dan konstruksi dari data warehouse untuk analisis data multidimensi dan data
mining.
– View perubahan debet dan pendapatan/keuntungan berdasarkan bulan, daerah,
sektor dan faktor.
– Akses informasi statistik seperti max, min, total, average, trend, dll.
• Peramalan/prediksi pembayaran pinjaman / analisis kebijaksanaan kredit konsumen.
– Pemeringkatan pemilihan fitur dan keterhubungan atribut
– Kinerja pembayaran pinjaman
– Rating kredit konsumen
Data Mining Keuangan
• Classification dan clustering dari konsumen untuk sasaran pemasaran.
– multidimensional segmentation melalui nearest-neighbor, classification, decision
trees, dll. untuk mengidentifikasi kelompok konsumen atau mengasosiasi satu
konsumen baru ke satu kelompok konsumen yang tepat/sesuai.
• Detection of money laundering dan kejahatan keuangan lainnya
– integration of from multiple DBs (e.g., bank transactions, federal/state crime
history DBs)
– Tools: data visualization, linkage analysis, classification, clustering tools, outlier
analysis, and sequential pattern analysis tools (find unusual access sequences)
Data Mining untuk Retail Industry
• Retail industry: jumlah data yang sangat besar pada sales, customer shopping history,
dll.
• Aplikasi dari retail data mining
– Identify customer buying behaviors
– Discover customer shopping patterns and trends
– Improve the quality of customer service
– Achieve better customer retention and satisfaction
– Enhance goods consumption ratios
– Design more effective goods transportation and distribution policies
Data Mining dalam Retail Industry
• Design and construction of data warehouses based on the benefits of data mining
– Multidimensional analysis of sales, customers, products, time, and region
• Analysis of the effectiveness of sales campaigns
• Customer retention: Analysis of customer loyalty
– Use customer loyalty card information to register sequences of purchases of
particular customers
– Use sequential pattern mining to investigate changes in customer consumption
or loyalty
– Suggest adjustments on the pricing and variety of goods
• Purchase recommendation and cross-reference of items
Data Mining untuk Industri Telekomunikasi
• A rapidly expanding and highly competitive industry and a great demand for data mining
– Understand the business involved
– Identify telecommunication patterns
– Catch fraudulent activities
– Make better use of resources
– Improve the quality of service
• Multidimensional analysis of telecommunication data
– Intrinsically multidimensional: calling-time, duration, location of caller, location of
callee, type of call, etc.
• Fraudulent pattern analysis and the identification of unusual patterns
– Identify potentially fraudulent users and their atypical usage patterns
– Detect attempts to gain fraudulent entry to customer accounts
– Discover unusual patterns which may need special attention
• Multidimensional association and sequential pattern analysis
– Find usage patterns for a set of communication services by customer group, by
month, etc.
– Promote the sales of specific services
– Improve the availability of particular services in a region
• Use of visualization tools in telecommunication data analysis
Bagaimana memilih satu Sistem Data Mining?
• Commercial data mining systems have little in common
– Different data mining functionality or methodology
– May even work with completely different kinds of data sets
• Need multiple dimensional view in selection
• Data types: relational, transactional, text, time sequence, spatial?
• System issues
– running on only one or on several operating systems?
– a client/server architecture?
– Provide Web-based interfaces and allow XML data as input and/or output?
• Data sources
– ASCII text files, multiple relational data sources
– support ODBC connections (OLE DB, JDBC)?
• Data mining functions and methodologies
– One vs. multiple data mining functions
– One vs. variety of methods per function
• More data mining functions and methods per function provide the user
with greater flexibility and analysis power
• Coupling with DB and/or data warehouse systems
– Four forms of coupling: no coupling, loose coupling, semitight coupling, and tight
coupling
Ideally, a data mining system should be tightly coupled with a database system
• Scalability
– Row (or database size) scalability
– Column (or dimension) scalability
– Curse of dimensionality: it is much more challenging to make a system column
scalable that row scalable
• Visualization tools
– “A picture is worth a thousand words”
– Visualization categories: data visualization, mining result visualization, mining
process visualization, and visual data mining
• Data mining query language and graphical user interface
– Easy-to-use and high-quality graphical user interface
– Essential for user-guided, highly interactive data mining
Contoh Sistem Data Mining
• IBM Intelligent Miner
– A wide range of data mining algorithms
– Scalable mining algorithms
– Toolkits: neural network algorithms, statistical methods, data preparation, and
data visualization tools
– Tight integration with IBM's DB2 relational database system
• SAS Enterprise Miner
– A variety of statistical analysis tools
– Data warehouse tools and multiple data mining algorithms
• Mirosoft SQLServer 2000
– Integrate DB and OLAP with mining
– Support OLEDB for DM standard
• SGI MineSet
– Multiple data mining algorithms and advanced statistics
– Advanced visualization tools
• Clementine (SPSS)
– An integrated data mining development environment for end-users and
developers
– Multiple data mining algorithms and visualization tools
• DBMiner (DBMiner Technology Inc.)
– Multiple data mining modules: discovery-driven OLAP analysis, association,
classification, and clustering
– Efficient, association and sequential-pattern mining functions, and visual
classification tool
– Mining both relational databases and data warehouses
Data Mining dan Intelligent Query Answering
• A general framework for the integration of data mining and intelligent query answering
– Data query: finds concrete data stored in a database; returns exactly what is
being asked
– Knowledge query: finds rules, patterns, and other kinds of knowledge in a
database
• Intelligent (or cooperative) query answering: analyzes the intent of the
query and provides generalized, neighborhood or associated information
relevant to the query
Trends dalam Data Mining
• Application exploration
– development of application-specific data mining system
– Invisible data mining (mining as built-in function)
• Scalable data mining methods
– Constraint-based mining: use of constraints to guide data mining systems in their
search for interesting patterns
• Integration of data mining with database systems, data warehouse systems, and Web
database systems
• Invisible data mining
• Standardization of data mining language
– A standard will facilitate systematic development, improve interoperability, and
promote the education and use of data mining systems in industry and society
• Visual data mining
• New methods for mining complex types of data
– More research is required towards the integration of data mining methods with
existing data analysis techniques for the complex types of data
• Web mining
• Privacy protection and information security in data mining
Sumber :
Modul Perkuliahan - Data Warehouse dan Data Mining - Program Studi Sistem Informasi - Fakultas Ilmu Komputer - Universitas Mercu Buana
