1 / 24

Data Preprocessing

Data Preprocessing. By K.K.Ezhilarasan , Asst Prof./CSE, SET,SGI. Outline. Introduction Descriptive Data Summarization Data Cleaning Missing value Noise data Data Integration Redundancy Data Transformation. Knowledge Discovery (KDD) Process. Knowledge. Pattern Evaluation.

brooke-vance
Download Presentation

Data Preprocessing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Preprocessing By K.K.Ezhilarasan, Asst Prof./CSE, SET,SGI

  2. Outline • Introduction • Descriptive Data Summarization • Data Cleaning • Missing value • Noise data • Data Integration • Redundancy • Data Transformation

  3. Knowledge Discovery (KDD) Process Knowledge Pattern Evaluation • Data mining—core of knowledge discovery process Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases

  4. Knowledge Process • Data cleaning – to remove noise and inconsistent data • Data integration – to combine multiple source • Data selection – to retrieve relevant data for analysis • Data transformation – to transform data into appropriate form for data mining • Data mining • Evaluation • Knowledge presentation

  5. Why Preprocess the data • Image that you are a manager at ALLElectronics and have been charger with analyzing the company’s data • Then you realize: • Several of the attributes for carious tuples have no recorded value • Some information you want is not on recorded • Some values are reported as incomplete, noisy, and inconsistent • Welcome to real world!!

  6. Why Data Preprocessing? • Data in the real world is dirty • incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data • e.g., occupation=“ ” • noisy: containing errors or outliers • e.g., Salary=“-10” • inconsistent: containing discrepancies in codes or names • e.g., Age=“42” Birthday=“03/07/1997” • e.g., Was rating “1,2,3”, now rating “A, B, C” • e.g., discrepancy between duplicate records

  7. Why Is Data Dirty? • Incomplete data may come from • “Not applicable” data value when collected • Different considerations between the time when the data was collected and when it is analyzed. • Human/hardware/software problems

  8. Why Is Data Dirty? • Noisy data (incorrect values) may come from • Faulty data collection instruments • Human or computer error at data entry • Errors in data transmission

  9. Why Is Data Dirty? • Inconsistent data may come from • Different data sources • Functional dependency violation (e.g., modify some linked data) • Duplicate records also need data cleaning

  10. Why Is Data Preprocessing Important? • No quality data, no quality mining results! • Quality decisions must be based on quality data • e.g., duplicate or missing data may cause incorrect or even misleading statistics. • Data extraction, cleaning, and transformation comprises the majority of the work of building a data warehouse

  11. 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

  12. Forms of Data Preprocessing

  13. Outline • Introduction • Descriptive Data Summarization • Data Cleaning • Missing value • Noise data • Data Integration • Redundancy • Data Transformation

  14. Descriptive data summarization • Motivation • To better understand the data: central tendency, variation and spread • Data dispersion characteristics • median, max, min, quantiles, outliers, variance, etc.

  15. Descriptive data summarization • Numerical dimensions correspond to sorted intervals • Data dispersion: analyzed with multiple granularities of precision • Boxplot or quantile analysis on sorted intervals

  16. Measuring the Central Tendency • Mean • Median • Mode • Value that occurs most frequently in the data • Dataset with one, two or three modes are respectively called unimodal, bimodal, and trimodal

  17. Symmetric vs. Skewed Data

  18. Measuring the Dispersion of Data • Quartiles, outliers and boxplots • The median is the 50th percentile • Quartiles: Q1 (25th percentile), Q3 (75th percentile) • Inter-quartile range (IQR): IQR = Q3 –Q1 • Outlier: usually, a value higher/lower than 1.5 x IQR

  19. Boxplot Analysis • Five-number summary of a distribution: Minimum, Q1, M, Q3, Maximum • Boxplot • Data is represented with a box • The ends of the box are at the first and third quartiles, i.e., the height of the box is IRQ • The median is marked by a line within the box • Whiskers: two lines outside the box extend to Minimum and Maximum

  20. Boxplot Analysis

  21. 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

  22. Histogram Analysis

  23. Quantile Plot • Displays all of the data (allowing the user to assess both the overall behavior and unusual occurrences) • Plots quantile information • For a data xidata sorted in increasing order, fiindicates that approximately 100 fi% of the data are below or equal to the value xi

  24. Quantile Plot

More Related