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GSU CSC 2010 - Chapter 2

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Data PreprocessingOutlineKnowledge Discovery (KDD) ProcessKnowledge ProcessWhy Preprocess the dataWhy Data Preprocessing?Why Is Data Dirty?Slide 8Slide 9Why Is Data Preprocessing Important?Major Tasks in Data PreprocessingForms of Data PreprocessingSlide 13Descriptive data summarizationSlide 15Measuring the Central TendencySymmetric vs. Skewed DataMeasuring the Dispersion of DataBoxplot AnalysisSlide 20Histogram AnalysisSlide 22Quantile PlotSlide 24Data PreprocessingDr. Bernard Chen Ph.D.University of Central ArkansasFall 2010OutlineIntroductionDescriptive Data SummarizationData CleaningMissing valueNoise dataData IntegrationRedundancyData TransformationKnowledge Discovery (KDD) ProcessData mining—core of knowledge discovery processData CleaningData IntegrationDatabasesData WarehouseTask-relevant DataSelectionData MiningPattern EvaluationKnowledge Process1. Data cleaning – to remove noise and inconsistent data2. Data integration – to combine multiple source 3. Data selection – to retrieve relevant data for analysis4. Data transformation – to transform data into appropriate form for data mining5. Data mining6. Evaluation7. Knowledge presentationWhy 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!!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 recordsWhy 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 problemsWhy Is Data Dirty?Noisy data (incorrect values) may come fromFaulty data collection instrumentsHuman or computer error at data entryErrors in data transmissionWhy 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 cleaningWhy 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 warehouseMajor 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 resultsForms of Data PreprocessingOutlineIntroductionDescriptive Data SummarizationData CleaningMissing valueNoise dataData IntegrationRedundancyData TransformationDescriptive data summarizationMotivationTo better understand the data: central tendency, variation and spreadData dispersion characteristics median, max, min, quantiles, outliers, variance, etc.Descriptive data summarizationNumerical dimensions correspond to sorted intervalsData dispersion: analyzed with multiple granularities of precisionBoxplot or quantile analysis on sorted intervalsMeasuring 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 trimodalSymmetric vs. Skewed DataMeasuring 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 IQRBoxplot 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 MaximumBoxplot AnalysisHistogram 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 dataHistogram AnalysisQuantile PlotDisplays all of the data (allowing the user to assess both the overall behavior and unusual occurrences)Plots quantile informationFor a data xi data sorted in increasing order, fi indicates that approximately 100 fi% of the data are below or equal to the value xiQuantile


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GSU CSC 2010 - Chapter 2

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