'Handle missing data' diaporamas de présentation

CS590D: Data Mining Chris Clifton

CS590D: Data Mining Chris Clifton. January 14, 2006 Data Preparation. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?.

1.73k views • 129 slides

1.38k views • 116 slides

1.16k views • 116 slides

1.16k views • 116 slides

Data Warehousing/Mining Comp 150 DW Chapter 3: Data Preprocessing

Data Warehousing/Mining Comp 150 DW Chapter 3: Data Preprocessing. Instructor: Dan Hebert. Chapter 3: Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary.

576 views • 57 slides

Data Preparation (Data pre-processing)

Data Preparation (Data pre-processing). Data Preparation. Introduction to Data Preparation Types of Data Outliers Data Transformation Missing Data. 2. INTRODUCTION TO DATA PREPARATION. 3. Why Prepare Data?. Some data preparation is needed for all mining tools

378 views • 37 slides

Outline

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Chapter 2: Data Preprocessing

Chapter 2: Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Data Cleaning. Importance

881 views • 21 slides

Missing Data

Missing Data. What do we mean by missing data?. Missing observations which were intended to be collected but: Never collected Lost accidently Wrongly collected so deleted Outcomes and/or Explanatory variables. Effect of Missing Data. Can cause

641 views • 19 slides

Data Preprocessing

141 views • 13 slides