Data Preprocessing UE 141 Spring 2013
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1 Data Preprocessing UE 141 Spring 2013 Jing Gao SUNY Buffalo 1
2 Outline Data Data Preprocessing Improve data quality Prepare data for analysis Exploring Data Statistics Visualization 2
3 Document Data Each document is a collection of words 3
4 Transaction Data A collection of transactions Each transaction involves a set of items TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk 4
5 Graph Data Aspirin Yeast protein interaction network Internet Co-author network 5
6 DNA Sequence Sequence Data GGTTCCGCCTTCAGCCCCGCGCC CGCAGGGCCCGCCCCGCGCCGTC GAGAAGGGCCCGCCTGGCGGGCG GGGGGAGGCGGGGCCGCCCGAGC CCAACCGAGTCCGACCAGGTGCC CCCTCTGCTCGGCCTAGACCTGA GCTCATTAGGCGGCAGCGGACAG GCCAAGTAGAACACGCGAAGCGC TGGGCTGCCTGCTGCGACCAGGG 6
7 Spatial-Temporal Data Average Monthly Temperature of land and ocean 7
8 10 Record Data Data that consists of a collection of records, each of which consists of a fixed set of attributes Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 8
9 10 Record Data Collection of data objects and their attributes Attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc. Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object Object is also known as record, point, case, sample, entity, or instance Objects Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 9
10 Types of Attribute Categorical Attribute Has only a finite or countably infinite set of values Examples: zip codes, weather conditions, or the set of words in a collection of documents Numerical Attribute Has real numbers as attribute values Examples: temperature, height, or weight 10
11 Outline Data Data Preprocessing Improve data quality Prepare data for analysis Exploring Data Statistics Visualization 11
12 Data Quality Issue Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest e.g., occupation= (missing data) noisy: containing noise, errors, or outliers e.g., Salary= 10 (an error) inconsistent: containing discrepancies in codes or names, e.g., Age= 42 Birthday= 03/07/1997 Was rating 1,2,3, now rating A, B, C discrepancy between duplicate records 12
13 Handle missing values Preprocessing Ignore the records with missing values Estimate missing values Remove outliers Find and remove those values that are significantly different from the others Resolve conflicts Merge information from different data sources Find duplicate records and identify the correct information 13
14 Prepare Data for Analysis Sampling Feature selection Dimensionality reduction Discretization 14
15 Sampling Goal Extract a subset of records so that the selected records are representative of original data 8000 points 2000 Points 500 Points 15
16 Attribute Selection Redundant attributes duplicate much or all of the information contained in one or more other attributes Example: purchase price of a product and the amount of sales tax paid Irrelevant attributes contain no information that is useful for the data mining task at hand Example: students' ID is often irrelevant to the task of predicting students' GPA 16
17 Dimensionality Reduction Goal is to reduce the number of attributes by creating a new set of attributes x 2 e x 1 17
18 Discretization Binning Convert numerical data into categorical data Divides the range into N intervals q Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into 3 bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34 18
19 Outline Data Data Preprocessing Improve data quality Prepare data for analysis Exploring Data Statistics Visualization 19
20 Statistics: Center Measurement Center measurement is a summary measure of the overall level of a dataset Mean: Summing up all the observation and dividing by number of observations. Mean of 20, 30, 40 is ( )/3 = 30. Notation : Let x, x x. Then the mean of this variable, x x + x x n 2,... x 1 2 n i= 1 = = 1 n n are n observations of n x i a variable 20
21 Statistics: Variability Measurement Variance: The variance of a set of observations is the average of the squares of the deviations of the observations from their mean. In symbols, the variance of the n observations x 1, x 2, x n is S 2 = ( x 1 - x) ( x n -1 n - x) 2 Variance of 5, 7, 3? Mean is (5+7+3)/3 = 5 and the variance is (5-5) 2 + (3-5) (7-5) 2 = 4 Standard Deviation: Square root of the variance. The standard deviation of the above example is 2. 21
22 Statistics: Frequency Distribution Consider a data set of 26 children of ages 1-6 years. Then the frequency distribution of variable age can be tabulated as follows: Frequency Distribution of Age Age Frequency Grouped Frequency Distribution of Age: Age Group Frequency
23 Data Visualization Question Choose a few creative and fascinating examples of data visualization to show to the class 23
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