Transcription of LECTURE 2: DATA (PRE-)PROCESSING
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LECTURE 2: DATA (PRE-)PROCESSING
LECTURE 2: data (PRE-)PROCESSINGDr. DhavalPatel CSE, In Previous Class, We discuss various type of data with examples In this Class, We focus on data pre- processing an important milestone of the data Mining Process data analysis pipeline Mining is not the only step in the analysis process Preprocessing: real data is noisy, incomplete and inconsistent. data cleaning is required to make sense of the data Techniques: Sampling, Dimensionality Reduction, Feature Selection. Post- processing : Make the data actionable and useful to the user : Statistical analysis of importance & PreprocessingData MiningResult Post-processingData Preprocessing Attribute Values Attribute Transformation Normalization (Standardization) Aggregation Discretization Sampling Dimensionality Reduction Feature subset selection Distance/Similarity Calculation VisualizationAttribute ValuesData is described usi
Reasons for missing values Information is not collected (e.g., people decline to give their age and weight) Attributes may not be applicable to all cases (e.g., annual income is not applicable to children) Handling missing values Eliminate Data Objects Estimate Missing Values Ignore the Missing Value During Analysis Replace with all possible ...
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