Data Mining: Concepts and Techniques. Chapter 2
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1 Data Mining: Concepts and Techniques Chapter 2 Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign Jiawei Han and Micheline Kamber, All rights reserved Data Mining: Concepts and Techniques 1
2 Data Mining: Concepts and Techniques 2
3 Chapter 2: Data Preprocessing Why preprocess the data? Data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary Data Mining: Concepts and Techniques 3
4 Mengapa Diperlukan Data Preprocessing? Data in the real world is dirty (tidak sempurna) incomplete: nilai atribut tidak lengkap, attribut yang seharusnya ada tidak ada, atau hanya data agrigasi yang tersedia (aggregate data) e.g., Occupation =, Jenis_kelamin = noisy: mengandung error atau outliers e.g., Gaji = inconsistent: terjadi perbedaan (discrepancies) dalam pengkodean dan nilai e.g., Age= 42 Birthday= 03/07/1980 e.g., Sebelumnya rating 1,2,3, sekarang A, B, C e.g., Terjadi perbedaan pada data yang duplikat Data Mining: Concepts and Techniques 4
5 Why Is Data Dirty? Incomplete data dapat terjadi karena Pada saat dikumpulkan, nilai dari atribut tertentu tidak tersedia not applicable Terjadi perbedaan pertimbangan sewaktu data dikumpulkan dengan sewaktu data dianalisa Problem yang disebabkan oleh manusia/hardware/software Noisy data (incorrect values) dapat terjadi karena Faulty data collection instruments (kesalahan pada alat) Human atau komputer error pada saat entry data Terjadi error pada saat dikirim (errors in data transmission) Inconsistent data dapat terjadi karena Perbedaan sumber data (different data sources) Pelanggaran ketergantungan fungsionalitas (functional dependency violation) e.g., modify some linked data Terjadinya Duplikasi Record (Data) Data Mining: Concepts and Techniques 5
6 Mengapa Data Preprocessing Penting? No quality data, no quality mining results! (Garbage in, garbage out) Keputusan yang baik harus berdasarkan data yang berkualitas pula (Quality decisions must be based on quality data) e.g., duplicate or missing data may cause incorrect or even misleading statistics Data warehouse membutuhkan gabungan data-data yang berkualitas Data extraction, cleaning, dan transformation merupakan bagian terpenting dari data warehouse Data Mining: Concepts and Techniques 6
7 Major Tasks in Data Preprocessing Data cleaning Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration Integration of multiple databases or files Data transformation Normalization and aggregation Data reduction Obtains reduced representation in volume but produces the same or similar analytical results Data discretization Part of data reduction but with particular importance, especially for numerical data Data Mining: Concepts and Techniques 7
8 Ilustrasi dari Beberapa Jenis Data Preprocessing Data Mining: Concepts and Techniques 8
9 Chapter 2: Data Preprocessing Why preprocess the data? Data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary Data Mining: Concepts and Techniques 9
10 Mengukur Nilai Tengah (Central Tendency) Mean: x 1 n n i 1 x i x N e.g: 4, 36, 45, 50, 75 Median: Middle value if odd number of values, or average of the middle two values otherwise e.g: 1, 5, 2, 8, 7 Mode e.g: 1, 3, 6, 6, 6, 6, 7, 7, 12, 12, 17 Value that occurs most frequently in the data Unimodal, bimodal, trimodal Empirical formula: mean mode 3 ( mean median ) Data Mining: Concepts and Techniques 10
11 Symmetric vs. Skewed Data Median, mean and mode of symmetric, positively and negatively skewed data Data Mining: Concepts and Techniques 11
12 Measuring the Dispersion of Data Quartiles, outliers ex: 9, 14, 17, 19, 22, 32, 35, 42, 99 Quartiles: Q 1 (25 th percentile), Q 3 (75 th percentile) Inter-quartile range: IQR = Q 3 Q 1 Five number summary: min, Q 1, M, Q 3, max Outlier: usually, a value higher/lower than 1.5 x IQR from median Variance and standard deviation Variance: (algebraic, scalable computation) 1 n n ( xi ) N i 1 N i 1 Standard deviation s (or σ) is the square root of variance σ 2 x 2 i 2 Data Mining: Concepts and Techniques 12
13 Properties of Normal Distribution Curve The normal (distribution) curve From μ σ to μ+σ: contains about 68% of the measurements (μ: mean, σ: standard deviation) From μ 2σ to μ+2σ: contains about 95% of it From μ 3σ to μ+3σ: contains about 99.7% of it Data Mining: Concepts and Techniques 13
14 Histogram Analysis Graph displays of basic statistical class descriptions Frequency histograms A univariate graphical method Consists of a set of rectangles that reflect the counts or frequencies of the classes present in the given data Data Mining: Concepts and Techniques 14
15 Positively and Negatively Correlated Data Data Mining: Concepts and Techniques 15
16 Chapter 2: Data Preprocessing Why preprocess the data? Data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary Data Mining: Concepts and Techniques 16
17 Data Cleaning Importance Data cleaning is one of the three biggest problems in data warehousing Ralph Kimball Data cleaning is the number one problem in data warehousing DCI survey Data cleaning tasks Fill in missing values Identify outliers and smooth out noisy data Correct inconsistent data Resolve redundancy caused by data integration Data Mining: Concepts and Techniques 17
18 Missing Data Data is not always available e.g., many tuples have no recorded value for several attributes, such as customer income in sales data Missing data disebabkan oleh: equipment malfunction inconsistent with other recorded data and thus deleted data not entered due to misunderstanding certain data may not be considered important at the time of entry Data Mining: Concepts and Techniques 18
19 How to Handle Missing Data? Ignore the tuple: usually done when class label is missing (assuming the tasks in classification not effective when the percentage of missing values per attribute varies considerably. Fill in the missing value manually: tedious + infeasible? Fill in it automatically with a global constant : e.g., unknown, a new class?! the attribute mean the attribute mean for all samples belonging to the same class: smarter the most probable value: hasil dari decision tree (klasifikasi) Data Mining: Concepts and Techniques 19
20 Noisy Data Incorrect attribute dapat disebabkan oleh faulty data collection instruments data entry problems data transmission problems Other data problems which requires data cleaning duplicate records incomplete data inconsistent data Data Mining: Concepts and Techniques 20
21 Bagaimana Mengatasi Noisy Data? Binning first sort data and partition into (equal-frequency) bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. Regression smooth by fitting the data into regression functions Clustering detect and remove outliers Combined computer and human inspection detect suspicious values and check by human (e.g., deal with possible outliers) Data Mining: Concepts and Techniques 21
22 Simple Discretization Methods: Binning Equal-width (distance) partitioning Divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B A)/N. The most straightforward, but outliers may dominate presentation Skewed data is not handled well Equal-depth (frequency) partitioning Divides the range into N intervals, each containing approximately same number of samples Good data scaling Managing categorical attributes can be tricky Data Mining: Concepts and Techniques 22
23 Binning Methods for Data Smoothing Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into equal-frequency (equi-depth) bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34 * Smoothing by bin means: - Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29 * Smoothing by bin boundaries: - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34 Data Mining: Concepts and Techniques 23
24 Regresi y Y1 Y1 y = x + 1 X1 x Data Mining: Concepts and Techniques 24
25 Pengelompokan Data Data Mining: Concepts and Techniques 25
26 Chapter 2: Data Preprocessing Why preprocess the data? Data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary Data Mining: Concepts and Techniques 26
27 Data integration: Data Integration Combines data from multiple sources into a coherent store Schema integration: e.g., A.cust-id B.cust-# Integrate metadata from different sources Entity identification problem: Identify real world entities from multiple data sources, e.g., Bill Clinton = William Clinton Detecting and resolving data value conflicts For the same real world entity, attribute values from different sources are different Possible reasons: different representations, different scales, e.g., metric vs. British units Data Mining: Concepts and Techniques 27
28 Mengatasi Redudansi saat Data Integrasi Redundant data occur often when integration of multiple databases Object identification: The same attribute or object may have different names in different databases Derivable data: One attribute may be a derived attribute in another table, e.g., annual revenue Redundant attributes may be able to be detected by correlation analysis Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality Data Mining: Concepts and Techniques 28
29 Analisa Korelasi untuk Data Numerik Correlation coefficient (also called Pearson s product moment coefficient) r A, B ( A A)( B B) ( n 1) ( AB) ( A B n 1) nab A B where n is the number of tuples, and are the respective means of A and B, σ A and σ B are the respective standard deviation of A and B, and Σ(AB) is the sum of the AB cross-product. If r A,B > 0, A and B are positively correlated (A s values increase as B s). The higher, the stronger correlation. r A,B = 0: independent; r A,B < 0: negatively correlated A B Data Mining: Concepts and Techniques 29
30 Transformasi Data Smoothing: remove noise from data Aggregation: summarization, data cube construction Generalization: concept hierarchy climbing Normalization: scaled to fall within a small, specified range min-max normalization z-score normalization normalization by decimal scaling Attribute/feature construction New attributes constructed from the given ones Data Mining: Concepts and Techniques 30
31 Data Transformation: Normalization Min-max normalization: to [new_min A, new_max A ] v' v mina maxa min Ex. Let income range $12,000 to $98,000 normalized to [0.0, 1.0]. Then $73,000 is mapped to Z-score normalization (μ: mean, σ: standard deviation): v' A A Ex. Let μ = 54,000, σ = 16,000. Then Normalization by decimal scaling v A ( new _ max A new _ mina) new _ min 73,600 12,000 (1.0 0) ,000 12,000 73,600 54,000 16, v v' Where j is the smallest integer such that Max( ν ) < 1 j 10 A Data Mining: Concepts and Techniques 31
32 Referensi D. P. Ballou and G. K. Tayi. Enhancing data quality in data warehouse environments. Communications of ACM, 42:73-78, 1999 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 T. Dasu, T. Johnson, S. Muthukrishnan, V. Shkapenyuk. Mining Database Structure; Or, How to Build a Data Quality Browser. SIGMOD 02. H.V. Jagadish et al., Special Issue on Data Reduction Techniques. Bulletin of the Technical Committee on Data Engineering, 20(4), December 1997 D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999 E. Rahm and H. H. Do. Data Cleaning: Problems and Current Approaches. IEEE Bulletin of the Technical Committee on Data Engineering. Vol.23, No.4 V. Raman and J. Hellerstein. Potters Wheel: An Interactive Framework for Data Cleaning and Transformation, VLDB 2001 T. Redman. Data Quality: Management and Technology. Bantam Books, 1992 Y. Wand and R. Wang. Anchoring data quality dimensions ontological foundations. Communications of ACM, 39:86-95, 1996 R. Wang, V. Storey, and C. Firth. A framework for analysis of data quality research. IEEE Trans. Knowledge and Data Engineering, 7: , 1995 Data Mining: Concepts and Techniques 32
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