Understanding Data Preprocessing in Data Mining Assignments

Feb 14, 2025

0 likes | 8 Views

Understanding Data Preprocessing in Data Mining Assignments explores the crucial steps of preparing raw data for analysis. This presentation covers key techniques such as data cleaning, transformation, normalization, and reduction to enhance data quality and improve mining accuracy. Learn how proper preprocessing can impact the overall efficiency and effectiveness of data mining models. Perfect for students seeking insights into data mining assignment services and online data mining assignment help to excel in their coursework.

Share Presentation

Embed Code

Link

Lucy30

Download Presentation

Understanding Data Preprocessing in Data Mining Assignments

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

Data Preprocessing

Data Preprocessing. Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009. Knowledge Discovery (KDD) Process. Knowledge. Pattern Evaluation. Data mining—core of knowledge discovery process. Data Mining. Task-relevant Data. Selection. Data Warehouse. Data Cleaning.

928 views • 29 slides

Data Preprocessing

Data Preprocessing. CSC 576: Data Science. Today. Data Preprocessing Handling Missing Values Handling Outliers Covariance and Correlation Normalization Binning, Discretization Sampling Aggregation. Data Preprocessing. Data Exploration phase results in finding data quality issues

954 views • 44 slides

Data preprocessing

Data preprocessing. Dr. Yan Liu Department of Biomedical, Industrial and Human Factors Engineering Wright State University. Why Data Preprocessing. “Dirty” Real-World Data Noisy: contain errors or outliers e.g. salary = “-10”

828 views • 42 slides

Data Preprocessing

Data Preprocessing . Lecturer: Dr. Bo Yuan E-mail: yuanb@sz.tsinghua.edu.cn. Outline. Data Cleansing Data Transformation Data Description Feature Selection Feature Extraction. Where are data from?. Why Data Preprocessing?. Real data are notoriously dirty!

978 views • 70 slides

Data Preprocessing

Data Preprocessing. Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Multi-Dimensional Measure of Data Quality. A well-accepted multidimensional view: Accuracy Completeness Consistency Timeliness

1.33k views • 48 slides

Data Preprocessing

Data Preprocessing. Peter Brezany Institu t für Softwarewissenschaften Universit ät Wien Tel. 4277 38825 Sprechstunde: Di, 12.30-13.30. Today‘s real-world databases are highly susceptible to noisy , missing , and inconsistent data .

322 views • 16 slides

Data Mining: Data Preprocessing

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

1.44k views • 76 slides

Data Preprocessing

Data Preprocessing. Lecture 12: Overview of data preprocessing Lecture 13: Descriptive data summarization Lecture 14: Data cleaning Lecture 15: Data integration/transformation and data reduction Lecture 16: Discretization and concept hierarchy generation and summary. Discretization.

362 views • 12 slides

Data Preprocessing

Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

1.29k views • 75 slides

Data Preprocessing

Data Preprocessing. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

877 views • 48 slides

Data Mining: Data Understanding

Data Mining: Data Understanding. Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar. What is Data?. Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc.

959 views • 68 slides

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.

447 views • 24 slides

Data Preprocessing

Data Preprocessing. CS 536 – Data Mining These slides are adapted from J. Han and M. Kamber’s book slides (http://www.cs.sfu.ca/~han). Representation of Data. Data can be represented in different ways Different types of values are used for attributes or features

720 views • 58 slides

Data Preprocessing

Data Preprocessing. Relational Databases - Normalization Denormalization Data Preprocessing Missing Data Missing values and the 3VL approach Problems with 3VL approach Special Values. Remember: Relational Databases. Model entities and relationships

766 views • 56 slides

Understanding Data Mining

Understanding Data Mining. Craig A. Stevens, PMP, CC craigastevens@westbrookstevens.com www.westbrookstevens.com. Examples of Classical Statistical Methods. Latitude 36.19N and Longitude -86.78W. Nashville, TN, USA. Y i = a + bx i + e. Multiple Regression.

426 views • 33 slides

Data Mining Assignments

Data Mining Assignments. Erik Zeitler Uppsala Database Laboratory. Oral exam. Two parts: Validation Your solution is validated using a script If your implementation does not work the examination ends immediately (“fail” grade is given) you may re-do the examination later Discussion

215 views • 19 slides

Data Preprocessing

Data Preprocessing. Chapter 2. Chapter Objectives. Realize the importance of data preprocessing for real world data before data mining or construction of data warehouses. Get an overview of some data preprocessing issues and techniques. The course. (4). DS. OLAP. (2). (3). Data

745 views • 63 slides

Data Preprocessing

Data Preprocessing. The need of data preprocessing. Problems with huge real-world database Incomplete data : missing value Noisy Inconsistent  Influence data mining process, especially pattern mined. Techniques. Data cleaning Data integration Data transformation Data reduction

173 views • 9 slides

Data Preprocessing

Data Preprocessing. Dr Lewis RSU Geography UCL. Rationale. when we view an RS image, we see a 'picture’. Rationale. when we view an RS image, we see a 'picture’. BUT need to be aware of the 'image formation process' to:

174 views • 11 slides

Data Preprocessing

187 views • 16 slides

Data PreProcessing

Data PreProcessing. Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation. Why Data Preprocessing?. Data in the real world is dirty

1.17k views • 101 slides

More Related