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Preparing the Data

Preparing the Data. What is Data?. Attributes. Kumpulan obyek data dan atributnya Atribut adalah property atau karakteristik suatu obyek Contoh : warna mata , temperature, dll Atribut dikenal sebagai variable, field, ataupun karakteristik Kumpulan dari atribut menggambarkan obyek

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Preparing the Data

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  1. Preparing the Data

  2. What is Data? Attributes • Kumpulan obyek data danatributnya • Atributadalahproperty ataukarakteristiksuatuobyek • Contoh: warnamata, temperature, dll • Atributdikenalsebagai variable, field, ataupunkarakteristik • Kumpulan dariatributmenggambarkanobyek • Obyekdikenaljugasebagai record, point, case, sample, entitas Objects

  3. Attribute Values • Nilaiatributadalah angka2 atau symbol2 ygdiassignkesuatuatribut • Perbedaanantaraatributdannilaiatribut • Atributygsamadapatdipetakkankenilaiatributygbeda • Misal: ketinggiandapatdiukurdalam feet atau meter • Atributygbedadapatdipetakankehimpunannilaiygsama • Contoh: nilaiatributuntuk ID dan age adalah integer • Tetapi property nilaiatributdapatberbeda: • ID tidakmempunyaibatasannilaimaksimumdan minimum

  4. Attribute Types • Ada jenis2 atributygberbeda: • Nominal • Contoh: nomor ID, warnamata, kode pos • Ordinal • Rangking/ tingkatan (contoh rasa darikripikkentangdalamskala 1-10), grade, tinggidalam {tinggi, sedang, rendah} • Interval • Contoh: tanggalkalender, temperature dalam Celsius atau Fahrenheit • Ratio • Contoh: temperature dalam Kelvin, panjang, waktu, jumlah

  5. Properties of Attribute Values /1 • Jenisatributtergantungpadapropertiberikutygmanadiamiliki • Distinctness: =  • Order: < > • Addition: + - • Multiplication: * / • Nominal attribute: distinctness • Ordinal attribute: distinctness & order • Interval attribute: distinctness, order & addition • Ratio attribute: all 4 properties

  6. Attribute Type Description Examples Operations Nominal The values of a nominal attribute are just different names, i.e., nominal attributes provide only enough information to distinguish one object from another. (=, ) zip codes, employee ID numbers, eye color, sex: {male, female} mode, entropy, contingency correlation, 2 test Ordinal The values of an ordinal attribute provide enough information to order objects. (<, >) hardness of minerals, {good, better, best}, grades, street numbers median, percentiles, rank correlation, run tests, sign tests Interval For interval attributes, the differences between values are meaningful, i.e., a unit of measurement exists. (+, - ) calendar dates, temperature in Celsius or Fahrenheit mean, standard deviation, Pearson's correlation, t and F tests Ratio For ratio variables, both differences and ratios are meaningful. (*, /) temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical current geometric mean, harmonic mean, percent variation Properties of Attribute Values /2

  7. Attribute Level Transformation Comments Nominal Any permutation of values If all employee ID numbers were reassigned, would it make any difference? Ordinal An order preserving change of values, i.e., new_value = f(old_value) where f is a monotonic function. An attribute encompassing the notion of good, better best can be represented equally well by the values {1, 2, 3} or by { 0.5, 1, 10}. Interval new_value =a * old_value + b where a and b are constants Thus, the Fahrenheit and Celsius temperature scales differ in terms of where their zero value is and the size of a unit (degree). Ratio new_value = a * old_value Length can be measured in meters or feet. Properties of Attribute Values / 3

  8. Discrete and Continuous Attributes • Discrete Attribute • Mempunyaihimpunannilaiterbatasatautakterbatas • Contoh: zip codes, himpunankatadalamkumpulandokumen • Seringdirepresentasikansbg variable integer • Note: binary attributes  special case • Continuos Attribute • Memiliki angka2 real sebagainilaiatribut • Contoh: temperatur, tinggiatauberat • Dapatdiukurdandirepresentasikanmenggunakansejumlah digit terbatas • Cirikhasnyadirepresentasikansebagai variable pecahan

  9. Asymmetric Attributes • Hanyakeberadaannya (non zero attribute value) diperhatikan • Contoh: • Kata-katamunculdidokumen • Item-item munculditransaksi customer

  10. Types of data sets • Record • Data Matrix • Document Data • Transaction Data • Graph • World Wide Web • Molecular Structures • Ordered • Spatial Data • Temporal Data • Sequential Data • Genetic Sequence Data

  11. Important characteristics of structured data • Dimensionality • Sparsity • Hanyamenghitungkemunculan • Resolution • Pola2 bergantungskala

  12. Record Data • Data ygberisikumpulan record, ygmanamasing-masingberisisuatuhimpunanatribut yang ditentukan.

  13. Data Matrix • Jikaobjek data mempunyaikumpulanatributnumerikygditentukan , kemudian data objekdapatdipandangsebagaititikdalamruang multidimensional, dimanasetiapdimensimerepresentasiansuatuatribut yang berbeda. • Seperti data set dapatdirepresentasikandengansuatumatrikm denganndimanaadam baris, satudarisetiapobjekdann kolom, satuuntuksetiapatribut.

  14. Document Data • Setiap document menjadisuatu ‘term’ vector, • Setiap term adalahkomponen (atribut) dari vector • Nilaisetiapkomponenadalahbanyaknyawaktuygberhubungan terms terdapatdalam document

  15. Transaction Data • Jenisspesialdari data rekord , dimana • Setiap record (transaksi) mencangkupkumpulan item-item • Contoh: Tokopenjualanbahanmakanan. Sejumlahprodukdibeli customer selamaperjalananpembelianmerupakansuatutransaksi, namunprodukygdibelimerupakan item

  16. Graph Data • Contoh: Generic graph and HTML Links

  17. Chemical Data • Benzene Molecule: C6H6

  18. Ordered Data /1 Items/Events • Sequence of transaction An element of the sequence

  19. Ordered Data /2 • Genomic sequence data

  20. Ordered Data /3 • Spatio-Temporal data Average Monthly Temperature of land and ocean

  21. Data Quality • Jenismasalahapakualitas data? • Bagaimanakitadapatmendeteksimasalahdengan data? • Apaygdapatkitalakukantentangmasalahini? • Contohmasalahkualitas data: • Noise & outliers • Missing Values • Duplicate data

  22. Noise • Mengacupadamodifikasinilai original • Contoh: distorsisuaraseseorangketikaberbicara Two Sine Waves Two Sine Waves + Noise

  23. Outliers /1 • Outliers adalahobyek data dengankarakteristikberbedadengankebanyakan data obyek lain dalam data set.

  24. Outliers /2 • Contoh: suatu data set merepresentasikangambaranumurdengan 20 nilaiygberbeda, • Age = {3, 56, 23, 39, 156, 52, 41, 22, 9, 28, 139, 31, 55, 20, -67, 37, 11, 55, 45, 37} • Maka parameter statistikaygberhubungan: • Mean = 39.9 • Standard deviation = 45.65 Jikakitamemilihnilai threshold untukdistribusi normal data : Theshold = Mean ± 2 x Standard Deviation makaseluruh data ygdiluar range [-54.1, 131.2] adalah potential outliers. Dan olehkarena age >0, mungkinmengurangi range menjadi [0, 131.2]. Sehinggaada outlier berdasarkankriteriaygdiberikan: 156, 139dan -67 Dengankemungkinanygtinggi, dapatdisimpulkan 3 data tersebutadamistypo (data ygdimasukkandenganpenambahan digit atautanda ‘-’)

  25. Missing Values • Beberapaalasan missing values: • Informasitidakterkumpul (misal: orang2 menolakmemberikan info umurdanberatmereka) • Atributmungkintidakdapatdiaplikasikan je semuakasus (misal: pendapatantidakdapatdiaplikasikanke anak2) • Mengatasi missing values: • Eliminasiobyek data • Mengestimasi missing value selamaanalisis • Menggantidengansemuanilaikemungkinan (pembobotanolehkemungkinannya)

  26. Duplicate Data • Data set mungkinterdapatobyek data yang duplikat, atauhampirduplikasidariyg lain • Isuutamadenganmenggabungkansumberyg berbeda2 • Contoh: orangygsamadenganberbagai email address • Data cleaning • Prosesperlakuandenganisu data duplikasi

  27. Data Preprocessing: Why is Needed? • Data diduniariilcenderungkotor • Incompete: kekurangannilaiatribut, kurangatributtttygmenarik, atauhanyaberupakumpulan data • Noise: berisi errors atau outliers • Inconsistent: berisiberbeda format dalam code dannama • Data ygtidakberkualitas, tidakada hasil2 mining ygberkualitas • Keputusankualitasharusdidasarkanpada data kualitas • Data warehouse memerlukanintegritaskonsistendari data kualitas

  28. Major task in Data Preprocessing • Data Cleaning • Data Integration • Data Transformation • Data Reduction • Data Discretisation

  29. Forms of Data Preprocessing

  30. Transforming Data • Centering • Mengurangisetiap data dengan rata2 darisetiapatribut • Normalization • Hasildari centering dibagidengan standard deviasi • Scaling • Merubah data sehinggaberadadalamskalatertentu

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