Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and prepare data for mining algorithms through techniques like discretization and concept hierarchy generation.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data by normalization, aggregation, and reduction. The document discusses techniques for data cleaning like binning and clustering to handle noisy data. It also covers data integration, transformation through normalization, and reduction using histograms, clustering, and sampling. Discretization and concept hierarchies are introduced as techniques to reduce continuous attributes for data analysis.
Data preprocessing is important for data mining and involves data cleaning, integration, reduction, and discretization. The goals are to handle missing data, remove noise, resolve inconsistencies, reduce data size for faster mining, and prepare data for modeling. Common techniques include filling in missing values, smoothing noisy data, aggregating data, normalizing values, selecting important features, clustering data, and discretizing continuous variables. Preprocessing helps produce higher quality mining results from dirtier real-world data.
This document discusses data preprocessing techniques. It defines data preprocessing as transforming raw data into an understandable format. Major tasks in data preprocessing are described as data cleaning, integration, transformation, and reduction. Data cleaning involves handling missing data, noisy data, and inconsistencies. Data integration combines data from multiple sources. Data transformation techniques include smoothing, aggregation, generalization, and normalization. The goal of data reduction is to reduce the volume of data while maintaining analytical results.
This document discusses various techniques for data preprocessing, including data cleaning, integration and transformation, reduction, and discretization. It provides details on techniques for handling missing data, noisy data, and data integration issues. It also describes methods for data transformation such as normalization, aggregation, and attribute construction. Finally, it outlines various data reduction techniques including cube aggregation, attribute selection, dimensionality reduction, and numerosity reduction.
The document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Specifically, it covers filling missing values, handling noisy data, data normalization, aggregation, attribute selection, clustering, sampling and entropy-based discretization to reduce data size while retaining important information.
This document discusses techniques for preprocessing data including data cleaning, transformation, integration, and reduction. Data cleaning removes noise and inconsistencies by filling missing values and identifying outliers. Data integration merges data from multiple sources while dealing with issues like different naming conventions. Data transformation techniques include normalization, aggregation, generalization, attribute selection, and dimensionality reduction to reduce data size and handle inconsistencies. Preprocessing is needed to clean noisy, inconsistent data and handle incomplete data from various sources.
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data by normalization, aggregation, and reduction. The document discusses techniques for data cleaning like binning and clustering to handle noisy data. It also covers data integration, transformation through normalization, and reduction using histograms, clustering, and sampling. Discretization and concept hierarchies are introduced as techniques to reduce continuous attributes for data analysis.
Data preprocessing is important for data mining and involves data cleaning, integration, reduction, and discretization. The goals are to handle missing data, remove noise, resolve inconsistencies, reduce data size for faster mining, and prepare data for modeling. Common techniques include filling in missing values, smoothing noisy data, aggregating data, normalizing values, selecting important features, clustering data, and discretizing continuous variables. Preprocessing helps produce higher quality mining results from dirtier real-world data.
This document discusses data preprocessing techniques. It defines data preprocessing as transforming raw data into an understandable format. Major tasks in data preprocessing are described as data cleaning, integration, transformation, and reduction. Data cleaning involves handling missing data, noisy data, and inconsistencies. Data integration combines data from multiple sources. Data transformation techniques include smoothing, aggregation, generalization, and normalization. The goal of data reduction is to reduce the volume of data while maintaining analytical results.
This document discusses various techniques for data preprocessing, including data cleaning, integration and transformation, reduction, and discretization. It provides details on techniques for handling missing data, noisy data, and data integration issues. It also describes methods for data transformation such as normalization, aggregation, and attribute construction. Finally, it outlines various data reduction techniques including cube aggregation, attribute selection, dimensionality reduction, and numerosity reduction.
The document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Specifically, it covers filling missing values, handling noisy data, data normalization, aggregation, attribute selection, clustering, sampling and entropy-based discretization to reduce data size while retaining important information.
This document discusses techniques for preprocessing data including data cleaning, transformation, integration, and reduction. Data cleaning removes noise and inconsistencies by filling missing values and identifying outliers. Data integration merges data from multiple sources while dealing with issues like different naming conventions. Data transformation techniques include normalization, aggregation, generalization, attribute selection, and dimensionality reduction to reduce data size and handle inconsistencies. Preprocessing is needed to clean noisy, inconsistent data and handle incomplete data from various sources.
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, and discretization.
Data cleaning involves filling in missing values, smoothing noisy data, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources by integrating schemas and resolving value conflicts. Data transformation techniques include normalization, aggregation, generalization, and smoothing.
Data reduction aims to reduce the volume of data while maintaining similar analytical results. This includes data cube aggregation, dimensionality reduction by removing unimportant attributes, data compression, and discretization which converts continuous attributes to categorical bins.
The document discusses data preprocessing tasks that are commonly performed on real-world databases before data mining or analysis. These tasks include data cleaning to handle incomplete, noisy, or inconsistent data through techniques like filling in missing values, identifying outliers, and resolving inconsistencies. Data integration is used to combine data from multiple sources by resolving attribute name differences and eliminating redundancies. Data transformation techniques like normalization, attribute construction, aggregation, and generalization are also discussed to convert data into appropriate forms for mining algorithms or users. The goal of these preprocessing steps is to improve the quality and consistency of data for subsequent analysis and knowledge discovery.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document discusses data preprocessing techniques for transforming raw data into an understandable format. It describes measures for data quality such as accuracy, completeness, and consistency. The major tasks in data preprocessing are outlined as data cleaning, integration, reduction, transformation, and discretization. Data cleaning involves handling missing values, noise, and inconsistencies. Data integration merges data from multiple sources to reduce redundancies and inconsistencies. Data reduction techniques include aggregation, attribute selection, and dimensionality reduction to obtain a smaller data representation. Data transformation consolidates data into appropriate forms for mining through techniques like smoothing, aggregation, generalization, and normalization. Data discretization divides continuous attributes into intervals to reduce data size and prepare for further analysis.
Data is often incomplete, noisy, and inconsistent which can negatively impact mining results. Effective data cleaning is needed to fill in missing values, identify and remove outliers, and resolve inconsistencies. Other important tasks include data integration, transformation, reduction, and discretization to prepare the data for mining and obtain reduced representation that produces similar analytical results. Proper data preparation is essential for high quality knowledge discovery.
Data preprocessing involves transforming raw data into a clean and understandable format. It includes data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers and resolves inconsistencies. Data integration combines data from multiple sources. Data transformation performs operations like normalization and aggregation. Data reduction obtains a reduced representation of data to improve mining performance without losing essential information.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, handle inconsistencies, and reduce data volume for analysis while retaining essential information. Techniques include discretization, concept hierarchy generation, sampling, clustering, and developing histograms to obtain a reduced data representation.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and convert continuous attributes to discrete intervals or concepts. Preprocessing helps produce higher quality mining results.
Data preprocessing is an important step for data mining and warehousing. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, and reducing data. The goals are to improve data quality, reduce data size, and prepare data for mining algorithms. Key techniques include data cleaning, discretization of continuous attributes, feature selection, and various data reduction methods like binning, clustering, and sampling. Preprocessing helps produce higher quality mining results from quality data.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes data integration and transformation through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality, handle inconsistencies, and reduce data size for mining. Techniques include binning, clustering, sampling and discretization which create intervals or concept hierarchies to generalize continuous attributes for analysis.
Data preprocessing is an important step for data mining and warehousing. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, and reducing data. The goals are to improve data quality, reduce data size, and prepare data for mining algorithms. Key techniques include data cleaning, discretization of continuous attributes, feature selection, and various data reduction methods like binning, clustering, and sampling. Preprocessing helps produce higher quality mining results based on higher quality input data.
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying and handling outliers, aggregating data, feature selection, normalization, binning, clustering, and generating concept hierarchies. Preprocessing addresses issues like dirty, incomplete, inconsistent or redundant data to improve mining quality and efficiency.
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying outliers, aggregating data, feature selection, binning, clustering, and generating concept hierarchies to replace raw values with semantic concepts. Preprocessing addresses issues like dirty, incomplete, inconsistent data to produce high quality input for mining models and decisions.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Common preprocessing tasks involve handling missing data, smoothing noisy data, and integrating data from multiple sources. Techniques like normalization, attribute construction, discretization, and dimensionality reduction are presented as methods for transforming and reducing data.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, and discretization.
Data cleaning involves filling in missing values, smoothing noisy data, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources by integrating schemas and resolving value conflicts. Data transformation techniques include normalization, aggregation, generalization, and smoothing.
Data reduction aims to reduce the volume of data while maintaining similar analytical results. This includes data cube aggregation, dimensionality reduction by removing unimportant attributes, data compression, and discretization which converts continuous attributes to categorical bins.
The document discusses data preprocessing tasks that are commonly performed on real-world databases before data mining or analysis. These tasks include data cleaning to handle incomplete, noisy, or inconsistent data through techniques like filling in missing values, identifying outliers, and resolving inconsistencies. Data integration is used to combine data from multiple sources by resolving attribute name differences and eliminating redundancies. Data transformation techniques like normalization, attribute construction, aggregation, and generalization are also discussed to convert data into appropriate forms for mining algorithms or users. The goal of these preprocessing steps is to improve the quality and consistency of data for subsequent analysis and knowledge discovery.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document discusses data preprocessing techniques for transforming raw data into an understandable format. It describes measures for data quality such as accuracy, completeness, and consistency. The major tasks in data preprocessing are outlined as data cleaning, integration, reduction, transformation, and discretization. Data cleaning involves handling missing values, noise, and inconsistencies. Data integration merges data from multiple sources to reduce redundancies and inconsistencies. Data reduction techniques include aggregation, attribute selection, and dimensionality reduction to obtain a smaller data representation. Data transformation consolidates data into appropriate forms for mining through techniques like smoothing, aggregation, generalization, and normalization. Data discretization divides continuous attributes into intervals to reduce data size and prepare for further analysis.
Data is often incomplete, noisy, and inconsistent which can negatively impact mining results. Effective data cleaning is needed to fill in missing values, identify and remove outliers, and resolve inconsistencies. Other important tasks include data integration, transformation, reduction, and discretization to prepare the data for mining and obtain reduced representation that produces similar analytical results. Proper data preparation is essential for high quality knowledge discovery.
Data preprocessing involves transforming raw data into a clean and understandable format. It includes data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers and resolves inconsistencies. Data integration combines data from multiple sources. Data transformation performs operations like normalization and aggregation. Data reduction obtains a reduced representation of data to improve mining performance without losing essential information.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, handle inconsistencies, and reduce data volume for analysis while retaining essential information. Techniques include discretization, concept hierarchy generation, sampling, clustering, and developing histograms to obtain a reduced data representation.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and convert continuous attributes to discrete intervals or concepts. Preprocessing helps produce higher quality mining results.
Data preprocessing is an important step for data mining and warehousing. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, and reducing data. The goals are to improve data quality, reduce data size, and prepare data for mining algorithms. Key techniques include data cleaning, discretization of continuous attributes, feature selection, and various data reduction methods like binning, clustering, and sampling. Preprocessing helps produce higher quality mining results from quality data.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes data integration and transformation through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality, handle inconsistencies, and reduce data size for mining. Techniques include binning, clustering, sampling and discretization which create intervals or concept hierarchies to generalize continuous attributes for analysis.
Data preprocessing is an important step for data mining and warehousing. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, and reducing data. The goals are to improve data quality, reduce data size, and prepare data for mining algorithms. Key techniques include data cleaning, discretization of continuous attributes, feature selection, and various data reduction methods like binning, clustering, and sampling. Preprocessing helps produce higher quality mining results based on higher quality input data.
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying and handling outliers, aggregating data, feature selection, normalization, binning, clustering, and generating concept hierarchies. Preprocessing addresses issues like dirty, incomplete, inconsistent or redundant data to improve mining quality and efficiency.
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying outliers, aggregating data, feature selection, binning, clustering, and generating concept hierarchies to replace raw values with semantic concepts. Preprocessing addresses issues like dirty, incomplete, inconsistent data to produce high quality input for mining models and decisions.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Common preprocessing tasks involve handling missing data, smoothing noisy data, and integrating data from multiple sources. Techniques like normalization, attribute construction, discretization, and dimensionality reduction are presented as methods for transforming and reducing data.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling. It addresses issues like missing values, noise, inconsistencies, and redundancy. Techniques include data cleaning (e.g. filling in missing values), integration, normalization, aggregation, dimensionality reduction, and discretization which reduces data volume while maintaining analytical ability. The goal is obtaining quality data for quality analysis and mining results.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling. It addresses issues like missing values, noise, inconsistencies, and redundancy. Techniques include data cleaning (e.g. filling in missing values), integration, normalization, aggregation, dimensionality reduction, and discretization which reduces data volume while maintaining analytical ability. The goal is obtaining quality data for quality analysis and mining results.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling. It addresses issues like missing values, noise, inconsistencies, and redundancy. Techniques include data cleaning (e.g. filling in missing values), integration, normalization, aggregation, dimensionality reduction, and discretization which reduces data volume while maintaining analytical ability. The goal is obtaining quality data for quality analysis and mining results.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
This document discusses data preprocessing techniques for data mining. It covers why preprocessing is important for obtaining quality mining results from quality data. The major tasks of data preprocessing are described, including data cleaning, integration, transformation, reduction, and discretization. Specific techniques for handling missing data, noisy data, and data integration are also outlined. The goals of data reduction strategies like dimensionality and numerosity reduction are explained.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for modeling and analysis. The document discusses several key aspects of data preprocessing including:
- Why data preprocessing is important to improve data quality and ensure accurate analysis results.
- Common data issues like missing values, noise, inconsistencies that require cleaning. Techniques for cleaning include filling in missing data, identifying and handling outliers, and resolving inconsistencies.
- Methods for reducing data like binning, regression, clustering, sampling to obtain a smaller yet representative version of the data.
- The major tasks in preprocessing like data cleaning, integration, transformation, reduction and discretization which are aimed at handling real-world data issues.
This document discusses data preparation techniques for data warehousing and mining projects, including descriptive data summarization, data cleaning, integration and transformation, and reduction. It covers cleaning techniques like handling missing data, identifying outliers, and resolving inconsistencies. Data integration challenges like schema matching and resolving conflicts are also addressed. Methods for data reduction like aggregation, generalization, normalization and attribute construction are summarized.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
2. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
3. Why Data Preprocessing?
Data in the real world is dirty
incomplete: lacking attribute values, lacking certain
attributes of interest, or containing only aggregate
data
noisy: containing errors or outliers
inconsistent: containing discrepancies in codes or
names
No quality data, no quality mining results!
Quality decisions must be based on quality data
Data warehouse needs consistent integration of
quality data
4. Multi-Dimensional Measure of
Data Quality
A well-accepted multidimensional view:
Accuracy
Completeness
Consistency
Timeliness
Believability
Value added
Interpretability
Accessibility
5. 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
Data discretization
Part of data reduction but with particular importance, especially
for numerical data
7. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
8. Data Cleaning
Data cleaning tasks
Fill in missing values
Identify outliers and smooth out noisy data
Correct inconsistent data
9. Missing Data
Data is not always available
E.g., many tuples have no recorded value for several
attributes, such as customer income in sales data
Missing data may be due to
equipment malfunction
inconsistent with other recorded data and thus deleted
data not entered due to misunderstanding
certain data may not be considered important at the time of
entry
not register history or changes of the data
Missing data may need to be inferred.
10. How to Handle
Missing Data?
Ignore the tuple: usually done when class label is missing
(assuming the tasks in classification—not effective when the
percentage of missing values per attribute varies considerably)
Fill in the missing value manually: tedious + infeasible?
Use a global constant to fill in the missing value: e.g., “unknown”, a
new class?!
Use the attribute mean to fill in the missing value
Use the most probable value to fill in the missing value: inference-
based such as Bayesian formula or decision tree
11. Noisy Data
Noise: random error or variance in a measured variable
Incorrect attribute values may due to
faulty data collection instruments
data entry problems
data transmission problems
technology limitation
inconsistency in naming convention
Other data problems which requires data cleaning
duplicate records
incomplete data
inconsistent data
12. How to Handle Noisy
Data?
Binning method:
first sort data and partition into (equi-depth) bins
then smooth by bin means, smooth by bin median,
smooth by bin boundaries, etc.
Clustering
detect and remove outliers
Combined computer and human inspection
detect suspicious values and check by human
Regression
smooth by fitting the data into regression functions
13. Simple Discretization
Methods: Binning
Equal-width (distance) partitioning:
It divides the range into N intervals of equal size: uniform grid
if A and B are the lowest and highest values of the attribute, the
width of intervals will be: W = (B-A)/N.
The most straightforward
But outliers may dominate presentation
Skewed data is not handled well.
Equal-depth (frequency) partitioning:
It divides the range into N intervals, each containing
approximately same number of samples
Good data scaling
Managing categorical attributes can be tricky.
14. Binning Methods for Data
Smoothing
* Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34
* Partition into (equi-depth) bins:
- Bin 1: 4, 8, 9, 15
- Bin 2: 21, 21, 24, 25
- Bin 3: 26, 28, 29, 34
* Smoothing by bin means:
- Bin 1: 9, 9, 9, 9
- Bin 2: 23, 23, 23, 23
- Bin 3: 29, 29, 29, 29
* Smoothing by bin boundaries:
- Bin 1: 4, 4, 4, 15
- Bin 2: 21, 21, 25, 25
- Bin 3: 26, 26, 26, 34
15. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
16. Data
Integration
Data integration:
combines data from multiple sources into a coherent
store
Schema integration
integrate metadata from different sources
Entity identification problem: identify real world entities
from multiple data sources, e.g., A.cust-id ≡ B.cust-#
Detecting and resolving data value conflicts
for the same real world entity, attribute values from
different sources are different
possible reasons: different representations, different
scales, e.g., metric vs. British units
17. Handling
Redundant Data
Redundant data occur often when integration of multiple
databases
The same attribute may have different names in
different databasesCareful integration of the data from
multiple sources may help reduce/avoid redundancies
and inconsistencies and improve mining speed and
quality
18. Data
Transformation
Smoothing: remove noise from data
Aggregation: summarization, data cube construction
Generalization: concept hierarchy climbing
Normalization: scaled to fall within a small, specified
range
min-max normalization
z-score normalization
normalization by decimal scaling
19. Data Transformation:
Normalization
min-max normalization
z-score normalization
normalization by decimal scaling
AAA
AA
A
minnewminnewmaxnew
minmax
minv
v _)__(' +−
−
−
=
A
A
devstand
meanv
v
_
'
−
=
j
v
v
10
'= Where j is the smallest integer such that Max(| |)<1'v
20. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
Summary
21. Data Reduction
Strategies
Warehouse may store terabytes of data: Complex
data analysis/mining may take a very long time to
run on the complete data set
Data reduction
Obtains a reduced representation of the data set that is
much smaller in volume but yet produces the same (or
almost the same) analytical results
Data reduction strategies
Data cube aggregation
Dimensionality reduction
Numerosity reduction
Discretization and concept hierarchy generation
22. Histograms
A popular data reduction
technique
Divide data into buckets
and store average (sum)
for each bucket
Can be constructed
optimally in one
dimension using dynamic
programming
Related to quantization
problems. 0
5
10
15
20
25
30
35
40
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
23. Clustering
Partition data set into clusters, and one can store cluster
representation only
Can be very effective if data is clustered but not if data
is “smeared”
Can have hierarchical clustering and be stored in multi-
dimensional index tree structures
There are many choices of clustering definitions and
clustering algorithms, further detailed in Chapter 8
24. Sampling
Allow a mining algorithm to run in complexity that is
potentially sub-linear to the size of the data
Choose a representative subset of the data
Simple random sampling may have very poor
performance in the presence of skew
Develop adaptive sampling methods
Stratified sampling:
Approximate the percentage of each class (or
subpopulation of interest) in the overall database
Used in conjunction with skewed data
26. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy generation
27. Discretization
Three types of attributes:
Nominal — values from an unordered set
Ordinal — values from an ordered set
Continuous — real numbers
Discretization:
divide the range of a continuous attribute into
intervals
Some classification algorithms only accept
categorical attributes.
Reduce data size by discretization
Prepare for further analysis
28. Discretization and Concept
hierachy
Discretization
reduce the number of values for a given continuous
attribute by dividing the range of the attribute into
intervals. Interval labels can then be used to replace
actual data values.
Concept hierarchies
reduce the data by collecting and replacing low level
concepts (such as numeric values for the attribute
age) by higher level concepts (such as young,
middle-aged, or senior).