ECT7110. Data Preprocessing. Prof. Wai Lam. ECT7110 Data Preprocessing 1
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1 ECT7110 Data Preprocessing Prof. Wai Lam ECT7110 Data Preprocessing 1
2 Why Data Preprocessing? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data noisy: containing errors or outliers inconsistent: containing discrepancies in codes or names No quality data, no quality mining results! Quality decisions must be based on quality data Data warehouse needs consistent integration of quality data ECT7110 Data Preprocessing 2
3 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, data cubes, 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 ECT7110 Data Preprocessing 3
4 Forms of data preprocessing ECT7110 Data Preprocessing 4
5 Data Cleaning Data cleaning tasks Fill in missing values Identify outliers and smooth out noisy data Correct inconsistent data ECT7110 Data Preprocessing 5
6 Data Transformation Smoothing: remove noise from data Aggregation: summarization, data cube construction 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 ECT7110 Data Preprocessing 6
7 Data Transformation: Normalization min-max normalization v mina v' = ( new_ maxa new_ mina) + maxa mina z-score normalization v mean v ' = stand_dev A A new_ min A ECT7110 Data Preprocessing 7
8 Normalization - Examples Suppose that the minimum and maximum values for attribute income are 12,000 and 98,000 respectively. How to map an income value of 73,600 to the range of [0.0,1.0]? Suppose that the man and standard deviation for the attribute income are 54,000 and 15,000. How to map an income value of 73,600 using z-score normalization? ECT7110 Data Preprocessing 8
9 Data Reduction Strategies Warehouse may store terabytes of data: Complex data analysis/mining may take a very long time to run on the complete data set Data reduction Obtains a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results Data reduction strategies Dimensionality reduction ECT7110 Data Preprocessing 9
10 Dimensionality Reduction Feature selection (i.e., attribute subset selection): Select a minimum set of features useful for data mining reduce # of patterns in the patterns, easier to understand ECT7110 Data Preprocessing 10
11 Histograms A popular data reduction technique Divide data into buckets and store average (sum) for each bucket Related to quantization problems ECT7110 Data Preprocessing 11
12 Histograms count count price ($) price ($) Singleton buckets Buckets denoting a Continuous range of values ECT7110 Data Preprocessing 12
13 Histograms How are buckets determined and the attribute values partitioned? - Equiwidth: The width of each bucket range is uniform - Equidepth: The buckets are created so that, roughly, the frequency of each bucket is constant ECT7110 Data Preprocessing 13
14 Histogram Examples Suppose that the values for the attribute age: 13, 15, 16, 16, 19, 20, 20, 21, 21, 22, 25, 25, , 30, 30, 30, 30, 32, 33, 33, 37, 40, 40, 40, 42, 42 Equiwidth Histogram: Equidepth Histogram: Bucket range Frequency Bucket range Frequency ECT7110 Data Preprocessing 14
15 Sampling Allow a mining algorithm to run in complexity that is potentially sub-linear to the size of the data Choose a representative subset of the data Simple random sampling may have very poor performance in the presence of skew Develop adaptive sampling methods Stratified sampling: Approximate the percentage of each class (or subpopulation of interest) in the overall database Used in conjunction with skewed data Sampling may not reduce database I/Os (page at a time). ECT7110 Data Preprocessing 15
16 Sampling SRSWOR (simple random sample without replacement) SRSWR Raw Data ECT7110 Data Preprocessing 16
17 T1 T2 T3 T4 T5 T6 T7 T8 SRSWOR (n 4) SRSWR (n 4) T5 T1 T8 T6 T4 T7 T4 T1 T101 T1 T2 T3... T T901 T Cluster sample (m 2) T201 T202 T T300 T701 T38 young young T38 T256 young T391 young T307 young T117 middle-aged T391 young T138 middle-aged T96 middle-aged T290 middle-aged T117 middle-aged T326 middle-aged T138 middle-aged T69 senior T263 middle-aged T290 middle-aged T308 middle-aged T326 middle-aged ECT7110 T387 middle-aged Data Preprocessing 17 T69 T284 senior senior Stratified sample (according to age)
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Contents 3 Data Preprocessing 3 3.1 Data Preprocessing: An Overview................. 4 3.1.1 Data Quality: Why Preprocess the Data?......... 4 3.1.2 Major Tasks in Data Preprocessing............. 5 3.2
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