This document provides an overview of data preprocessing techniques. It discusses data quality issues like missing values, noise, and inconsistencies that require cleaning. Major tasks in preprocessing include data cleaning, integration, reduction, and transformation. Data cleaning techniques are described for handling incomplete, noisy, and inconsistent data. Methods for data integration, reduction through dimensionality reduction and sampling, and transformation through normalization and discretization are also summarized.
This document provides an overview of data preprocessing techniques for data mining. It discusses data quality issues like accuracy, completeness, and consistency that require data cleaning. The major tasks of data preprocessing are described as data cleaning, integration, reduction, and transformation. Specific techniques for handling missing data, noisy data, and reducing redundancy during data integration are also summarized.
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.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses data preprocessing, which includes data cleaning, integration, reduction, and transformation. Data cleaning deals with handling missing, noisy, and inconsistent data. Data integration combines data from multiple sources. Data reduction reduces data volume for analysis through techniques like dimensionality reduction. Data transformation normalizes and discretizes values.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses the importance of data preprocessing, which includes tasks like data cleaning, integration, reduction, and transformation. Specific techniques are described for handling missing/noisy data, data integration when combining multiple sources, and reducing dimensionality through feature selection or dimension reduction methods like PCA. The goal of preprocessing is to prepare raw data into a format suitable for data mining algorithms.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses the importance of data preprocessing, which includes tasks like data cleaning, integration, reduction, and transformation. Specific techniques are described for handling missing/noisy data, data integration when combining multiple sources, and reducing dimensionality through feature selection or dimension reduction methods like PCA. The goal of preprocessing is to prepare raw data into a format suitable for data mining algorithms.
This document provides an overview of data preprocessing techniques for data mining. It discusses data quality issues like accuracy, completeness, and consistency that require data cleaning. The major tasks of data preprocessing are described as data cleaning, integration, reduction, and transformation. Specific techniques for handling missing data, noisy data, and reducing redundancy during data integration are also summarized.
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.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses data preprocessing, which includes data cleaning, integration, reduction, and transformation. Data cleaning deals with handling missing, noisy, and inconsistent data. Data integration combines data from multiple sources. Data reduction reduces data volume for analysis through techniques like dimensionality reduction. Data transformation normalizes and discretizes values.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses the importance of data preprocessing, which includes tasks like data cleaning, integration, reduction, and transformation. Specific techniques are described for handling missing/noisy data, data integration when combining multiple sources, and reducing dimensionality through feature selection or dimension reduction methods like PCA. The goal of preprocessing is to prepare raw data into a format suitable for data mining algorithms.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses the importance of data preprocessing, which includes tasks like data cleaning, integration, reduction, and transformation. Specific techniques are described for handling missing/noisy data, data integration when combining multiple sources, and reducing dimensionality through feature selection or dimension reduction methods like PCA. The goal of preprocessing is to prepare raw data into a format suitable for data mining algorithms.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses the importance of data preprocessing, which includes tasks like data cleaning, integration, reduction, and transformation. Specific techniques are described for handling missing/noisy data, data integration when combining multiple sources, and reducing dimensionality through feature selection or dimension reduction methods like PCA. The goal of preprocessing is to prepare raw data into a format suitable for data mining algorithms.
Data Preprocessing and Visualizsdjvnovrnververdfvdfationwokati2689
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Methods like wavelet transforms and principal component analysis map data to a new space to emphasize clustered regions and remove outliers or unimportant attributes.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document discusses data preprocessing techniques for data mining. It covers data cleaning, integration, reduction, transformation, and discretization. Data cleaning involves handling missing, noisy, and inconsistent data through techniques like filling in missing values, smoothing noisy data, and resolving inconsistencies. Data integration combines data from multiple sources. Data reduction reduces data size through dimensionality reduction, numerosity reduction, and compression. Dimensionality reduction techniques include wavelet transforms and principal component analysis.
Data Preprocessing can be defined as a process of converting raw data into a format that is understandable and usable for further analysis. It is an important step in the Data Preparation stage. It ensures that the outcome of the analysis is accurate, complete, and consistent.
The document discusses various techniques for data preprocessing, including data cleaning, integration, reduction, and transformation. It describes why preprocessing is important for improving data quality, accuracy, and consistency. Several forms of data preprocessing are covered in detail, such as handling missing or noisy data, data integration, dimensionality reduction techniques like principal component analysis, and different strategies for data reduction.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for analysis. It addresses issues like missing values, inconsistencies, noise and redundancy. Key tasks include data cleaning to detect and correct errors, data integration to combine related data from multiple sources, and data reduction to reduce dimensionality or data size for more efficient analysis while retaining important information. Techniques like wavelet transforms, principal component analysis and dimensionality reduction are commonly used for data reduction. Preprocessing aims to improve data quality and prepare it for downstream analysis tasks.
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 presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
The document discusses data preprocessing concepts from Chapter 3 of the book "Data Mining: Concepts and Techniques". It covers topics like data quality, major tasks in preprocessing including data cleaning, integration and reduction. Data cleaning involves handling incomplete, noisy and inconsistent data using techniques such as imputation of missing values, smoothing of noisy data, and resolving inconsistencies. Data integration combines data from multiple sources which requires tasks like schema integration and entity identification. Data reduction techniques include dimensionality reduction and data compression.
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.
This document provides an overview of data preprocessing techniques. It discusses why preprocessing is important, including that real-world data is often dirty, incomplete, noisy, and inconsistent. The major tasks of preprocessing are described as data cleaning, integration, transformation, reduction, and discretization. Specific techniques for handling missing data, noisy data, and reducing redundancy are also summarized.
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 include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses the importance of data preprocessing, which includes tasks like data cleaning, integration, reduction, and transformation. Specific techniques are described for handling missing/noisy data, data integration when combining multiple sources, and reducing dimensionality through feature selection or dimension reduction methods like PCA. The goal of preprocessing is to prepare raw data into a format suitable for data mining algorithms.
Data Preprocessing and Visualizsdjvnovrnververdfvdfationwokati2689
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Methods like wavelet transforms and principal component analysis map data to a new space to emphasize clustered regions and remove outliers or unimportant attributes.
This document provides an overview of data preprocessing techniques discussed in Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It covers topics such as data quality, data cleaning, data integration, data reduction, and data transformation. Data reduction techniques like dimensionality reduction aim to obtain a reduced representation of data that uses less space but produces similar analytical results. Dimensionality reduction methods include wavelet transforms, principal component analysis, and feature selection. Wavelet transforms decompose a signal into different frequency subbands and allow clusters to become more distinguishable at different resolution levels.
This document discusses data preprocessing techniques for data mining. It covers data cleaning, integration, reduction, transformation, and discretization. Data cleaning involves handling missing, noisy, and inconsistent data through techniques like filling in missing values, smoothing noisy data, and resolving inconsistencies. Data integration combines data from multiple sources. Data reduction reduces data size through dimensionality reduction, numerosity reduction, and compression. Dimensionality reduction techniques include wavelet transforms and principal component analysis.
Data Preprocessing can be defined as a process of converting raw data into a format that is understandable and usable for further analysis. It is an important step in the Data Preparation stage. It ensures that the outcome of the analysis is accurate, complete, and consistent.
The document discusses various techniques for data preprocessing, including data cleaning, integration, reduction, and transformation. It describes why preprocessing is important for improving data quality, accuracy, and consistency. Several forms of data preprocessing are covered in detail, such as handling missing or noisy data, data integration, dimensionality reduction techniques like principal component analysis, and different strategies for data reduction.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for analysis. It addresses issues like missing values, inconsistencies, noise and redundancy. Key tasks include data cleaning to detect and correct errors, data integration to combine related data from multiple sources, and data reduction to reduce dimensionality or data size for more efficient analysis while retaining important information. Techniques like wavelet transforms, principal component analysis and dimensionality reduction are commonly used for data reduction. Preprocessing aims to improve data quality and prepare it for downstream analysis tasks.
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 presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
The document discusses data preprocessing concepts from Chapter 3 of the book "Data Mining: Concepts and Techniques". It covers topics like data quality, major tasks in preprocessing including data cleaning, integration and reduction. Data cleaning involves handling incomplete, noisy and inconsistent data using techniques such as imputation of missing values, smoothing of noisy data, and resolving inconsistencies. Data integration combines data from multiple sources which requires tasks like schema integration and entity identification. Data reduction techniques include dimensionality reduction and data compression.
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.
This document provides an overview of data preprocessing techniques. It discusses why preprocessing is important, including that real-world data is often dirty, incomplete, noisy, and inconsistent. The major tasks of preprocessing are described as data cleaning, integration, transformation, reduction, and discretization. Specific techniques for handling missing data, noisy data, and reducing redundancy are also summarized.
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 include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
DevOps Consulting Company | Hire DevOps Servicesseospiralmantra
Spiral Mantra excels in providing comprehensive DevOps services, including Azure and AWS DevOps solutions. As a top DevOps consulting company, we offer controlled services, cloud DevOps, and expert consulting nationwide, including Houston and New York. Our skilled DevOps engineers ensure seamless integration and optimized operations for your business. Choose Spiral Mantra for superior DevOps services.
https://www.spiralmantra.com/devops/
Boost Your Savings with These Money Management AppsJhone kinadey
A money management app can transform your financial life by tracking expenses, creating budgets, and setting financial goals. These apps offer features like real-time expense tracking, bill reminders, and personalized insights to help you save and manage money effectively. With a user-friendly interface, they simplify financial planning, making it easier to stay on top of your finances and achieve long-term financial stability.
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
The Rising Future of CPaaS in the Middle East 2024Yara Milbes
Explore "The Rising Future of CPaaS in the Middle East in 2024" with this comprehensive PPT presentation. Discover how Communication Platforms as a Service (CPaaS) is transforming communication across various sectors in the Middle East.
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...kalichargn70th171
Visual testing plays a vital role in ensuring that software products meet the aesthetic requirements specified by clients in functional and non-functional specifications. In today's highly competitive digital landscape, users expect a seamless and visually appealing online experience. Visual testing, also known as automated UI testing or visual regression testing, verifies the accuracy of the visual elements that users interact with.
Transforming Product Development using OnePlan To Boost Efficiency and Innova...OnePlan Solutions
Ready to overcome challenges and drive innovation in your organization? Join us in our upcoming webinar where we discuss how to combat resource limitations, scope creep, and the difficulties of aligning your projects with strategic goals. Discover how OnePlan can revolutionize your product development processes, helping your team to innovate faster, manage resources more effectively, and deliver exceptional results.
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...Paul Brebner
Closing talk for the Performance Engineering track at Community Over Code EU (Bratislava, Slovakia, June 5 2024) https://eu.communityovercode.org/sessions/2024/why-apache-kafka-clusters-are-like-galaxies-and-other-cosmic-kafka-quandaries-explored/ Instaclustr (now part of NetApp) manages 100s of Apache Kafka clusters of many different sizes, for a variety of use cases and customers. For the last 7 years I’ve been focused outwardly on exploring Kafka application development challenges, but recently I decided to look inward and see what I could discover about the performance, scalability and resource characteristics of the Kafka clusters themselves. Using a suite of Performance Engineering techniques, I will reveal some surprising discoveries about cosmic Kafka mysteries in our data centres, related to: cluster sizes and distribution (using Zipf’s Law), horizontal vs. vertical scalability, and predicting Kafka performance using metrics, modelling and regression techniques. These insights are relevant to Kafka developers and operators.
Photoshop Tutorial for Beginners (2024 Edition)alowpalsadig
Photoshop Tutorial for Beginners (2024 Edition)
Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."
Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
Photoshop Tutorial for Beginners (2024 Edition)Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
The importance of developing and designing programming in 2024
Programming design and development represents a vital step in keeping pace with technological advancements and meeting ever-changing market needs. This course is intended for anyone who wants to understand the fundamental importance of software development and design, whether you are a beginner or a professional seeking to update your knowledge.
Course objectives:
1. **Learn about the basics of software development:
- Understanding software development processes and tools.
- Identify the role of programmers and designers in software projects.
2. Understanding the software design process:
- Learn about the principles of good software design.
- Discussing common design patterns such as Object-Oriented Design.
3. The importance of user experience (UX) in modern software:
- Explore how user experience can improve software acceptance and usability.
- Tools and techniques to analyze and improve user experience.
4. Increase efficiency and productivity through modern development tools:
- Access to the latest programming tools and languages used in the industry.
- Study live examples of applications
Manyata Tech Park Bangalore_ Infrastructure, Facilities and Morenarinav14
Located in the bustling city of Bangalore, Manyata Tech Park stands as one of India’s largest and most prominent tech parks, playing a pivotal role in shaping the city’s reputation as the Silicon Valley of India. Established to cater to the burgeoning IT and technology sectors
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
React.js, a JavaScript library developed by Facebook, has gained immense popularity for building user interfaces, especially for single-page applications. Over the years, React has evolved and expanded its capabilities, becoming a preferred choice for mobile app development. This article will explore why React.js is an excellent choice for the Best Mobile App development company in Noida.
Visit Us For Information: https://www.linkedin.com/pulse/what-makes-reactjs-stand-out-mobile-app-development-rajesh-rai-pihvf/
2. 2
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Chapter 3: Data Preprocessing
Data Preprocessing: An Overview
Data Quality
Major Tasks in Data Preprocessing
Data Cleaning
Data Integration
Data Reduction
Data Transformation and Data Discretization
Summary
3. 3
Data Quality: Why Preprocess the Data?
Measures for data quality: A multidimensional view
Accuracy: correct or wrong, accurate or not
Completeness: not recorded, unavailable, …
Consistency: some modified but some not, dangling, …
Timeliness: timely update?
Believability: how trustable the data are correct?
Interpretability: how easily the data can be
understood?
4. 4
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 reduction
Dimensionality reduction
Numerosity reduction
Data compression
Data transformation and data discretization
Normalization
Concept hierarchy generation
5. 5
5
Chapter 3: Data Preprocessing
Data Preprocessing: An Overview
Data Quality
Major Tasks in Data Preprocessing
Data Cleaning
Data Integration
Data Reduction
Data Transformation and Data Discretization
Summary
6. 6
Data Cleaning
Data in the Real World Is Dirty: Lots of potentially incorrect data,
e.g., instrument faulty, human or computer error, transmission error
incomplete: lacking attribute values, lacking certain attributes of
interest, or containing only aggregate data
e.g., Occupation=“ ” (missing data)
noisy: containing noise, errors, or outliers
e.g., Salary=“−10” (an error)
inconsistent: containing discrepancies in codes or names, e.g.,
Age=“42”, Birthday=“03/07/2010”
Was rating “1, 2, 3”, now rating “A, B, C”
discrepancy between duplicate records
Intentional (e.g., disguised missing data)
Jan. 1 as everyone’s birthday?
7. 7
Incomplete (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
8. 8
How to Handle Missing Data?
Ignore the tuple: usually done when class label is missing
(when doing classification)—not effective when the % of
missing values per attribute varies considerably
Fill in the missing value manually: tedious + infeasible?
Fill in it automatically with
a global constant : e.g., “unknown”, a new class?!
the attribute mean
the attribute mean for all samples belonging to the
same class: smarter
the most probable value: inference-based such as
Bayesian formula or decision tree
9. 9
Noisy Data
Noise: random error or variance in a measured variable
Incorrect attribute values may be due to
faulty data collection instruments
data entry problems
data transmission problems
technology limitation
inconsistency in naming convention
Other data problems which require data cleaning
duplicate records
incomplete data
inconsistent data
10. 10
How to Handle Noisy Data?
Binning
first sort data and partition into (equal-frequency) bins
then one can smooth by bin means, smooth by bin
median, smooth by bin boundaries, etc.
Regression
smooth by fitting the data into regression functions
Clustering
detect and remove outliers
Combined computer and human inspection
detect suspicious values and check by human (e.g.,
deal with possible outliers)
11. 11
11
Chapter 3: Data Preprocessing
Data Preprocessing: An Overview
Data Quality
Major Tasks in Data Preprocessing
Data Cleaning
Data Integration
Data Reduction
Data Transformation and Data Discretization
Summary
12. 12
12
Data Integration
Data integration:
Combines data from multiple sources into a coherent store
Schema integration: e.g., A.cust-id B.cust-#
Integrate metadata from different sources
Entity identification problem:
Identify real world entities from multiple data sources, e.g., Bill
Clinton = William Clinton
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.,
meter v.s. kilometer
13. 13
13
Handling Redundancy in Data Integration
Redundant data occur often when integration of multiple
databases
Object identification: The same attribute or object
may have different names in different databases
Derivable data: One attribute may be a “derived”
attribute in another table, e.g., annual revenue
Redundant attributes may be able to be detected by
correlation analysis and covariance analysis
Careful integration of the data from multiple sources may
help reduce/avoid redundancies and inconsistencies and
improve mining speed and quality
14. 14
Correlation Analysis (Nominal Data)
Χ2 (chi-square) test
The larger the Χ2 value, the more likely the variables are
related
The cells that contribute the most to the Χ2 value are
those whose actual count is very different from the
expected count
Correlation does not imply causality
# of hospitals and # of car-theft in a city are correlated
Both are causally linked to the third variable: population
Expected
Expected
Observed 2
2 )
(
15. 15
Chi-Square Calculation: An Example
Χ2 (chi-square) calculation (numbers in parenthesis are
expected counts calculated based on the data distribution
in the two categories)
It shows that like_science_fiction and play_chess are
correlated in the group
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Like science fiction 250(90) 200(360) 450
Not like science fiction 50(210) 1000(840) 1050
Sum(col.) 300 1200 1500
16. 16
Correlation Analysis (Numeric Data)
Correlation coefficient (also called Pearson’s product
moment coefficient)
where n is the number of tuples, and are the respective
means of A and B, σA and σB are the respective standard deviation
of A and B, and Σ(aibi) is the sum of the AB cross-product.
If rA,B > 0, A and B are positively correlated (A’s values
increase as B’s). The higher, the stronger correlation.
rA,B = 0: independent; rAB < 0: negatively correlated
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18. 18
Covariance (Numeric Data)
Covariance is similar to correlation
where n is the number of tuples, and are the respective mean or
expected values of A and B, σA and σB are the respective standard
deviation of A and B.
Positive covariance: If CovA,B > 0, then A and B both tend to be larger
than their expected values.
Negative covariance: If CovA,B < 0 then if A is larger than its expected
value, B is likely to be smaller than its expected value.
Independence: CovA,B = 0 but the converse is not true:
Some pairs of random variables may have a covariance of 0 but are not
independent. Only under some additional assumptions (e.g., the data follow
multivariate normal distributions) does a covariance of 0 imply independence
A B
Correlation coefficient:
19. Co-Variance: An Example
It can be simplified in computation as
Suppose two stocks A and B have the following values in one week:
(2, 5), (3, 8), (5, 10), (4, 11), (6, 14).
Question: If the stocks are affected by the same industry trends, will
their prices rise or fall together?
E(A) = (2 + 3 + 5 + 4 + 6)/ 5 = 20/5 = 4
E(B) = (5 + 8 + 10 + 11 + 14) /5 = 48/5 = 9.6
Cov(A,B) = (2×5+3×8+5×10+4×11+6×14)/5 − 4 × 9.6 = 4
Thus, A and B rise together since Cov(A, B) > 0.
20. 20
20
Chapter 3: Data Preprocessing
Data Preprocessing: An Overview
Data Quality
Major Tasks in Data Preprocessing
Data Cleaning
Data Integration
Data Reduction
Data Transformation and Data Discretization
Summary
21. 21
Data Reduction Strategies
Data reduction: Obtain a reduced representation of the data set that
is much smaller in volume but yet produces the same (or almost the
same) analytical results
Why data reduction? — A database/data warehouse may store
terabytes of data. Complex data analysis may take a very long time to
run on the complete data set.
Data reduction strategies
Dimensionality reduction, e.g., remove unimportant attributes
Principal Components Analysis (PCA)
Feature subset selection, feature creation
Numerosity reduction (some simply call it: Data Reduction)
Regression and Log-Linear Models
Histograms, clustering, sampling
Data compression
22. 22
Data Reduction 1: Dimensionality Reduction
Curse of dimensionality
When dimensionality increases, data becomes increasingly sparse
Density and distance between points, which is critical to clustering, outlier
analysis, becomes less meaningful
The possible combinations of subspaces will grow exponentially
Dimensionality reduction
Avoid the curse of dimensionality
Help eliminate irrelevant features and reduce noise
Reduce time and space required in data mining
Dimensionality reduction techniques
Principal Component Analysis
Supervised and nonlinear techniques (e.g., feature selection)
23. 23
Attribute Creation (Feature Generation)
Create new attributes (features) that can capture the
important information in a data set more effectively than
the original ones
Three general methodologies
Attribute extraction
Domain-specific
Mapping data to new space (see: data reduction)
Using kernel functions
Combining features
Data discretization
24. 24
Clustering
Partition data set into clusters based on similarity, and
store cluster representation (e.g., centroid and diameter)
only
Can have hierarchical clustering and be stored in multi-
dimensional index tree structures
There are many choices of clustering definitions and
clustering algorithms
25. 25
Sampling
Sampling: obtaining a small sample s to represent the
whole data set N
Allow a mining algorithm to run in complexity that is
potentially sub-linear to the size of the data
Key principle: Choose a representative subset of the data
Simple random sampling may have very poor
performance in the presence of skew
Develop adaptive sampling methods, e.g., stratified
sampling
26. 26
Types of Sampling
Simple random sampling
There is an equal probability of selecting any particular
item
Sampling without replacement
Once an object is selected, it is removed from the
population
Sampling with replacement
A selected object is not removed from the population
Stratified sampling:
Partition the data set, and draw samples from each
partition (proportionally, i.e., approximately the same
percentage of the data)
Used in conjunction with skewed data
29. 29
Data Reduction 3: Data Compression
String compression
There are extensive theories and well-tuned algorithms
Typically lossless, but only limited manipulation is
possible without expansion
Audio/video compression
Typically lossy compression, with progressive refinement
Sometimes small fragments of signal can be
reconstructed without reconstructing the whole
Dimensionality and numerosity reduction may also be
considered as forms of data compression
31. 31
Chapter 3: Data Preprocessing
Data Preprocessing: An Overview
Data Quality
Major Tasks in Data Preprocessing
Data Cleaning
Data Integration
Data Reduction
Data Transformation and Data Discretization
Summary
32. 32
Data Transformation
A function that maps the entire set of values of a given attribute to a
new set of replacement values s.t. each old value can be identified
with one of the new values
Methods
Smoothing: Remove noise from data
Attribute/feature construction
New attributes constructed from the given ones
Aggregation: Summarization, data cube construction
Normalization: Scaled to fall within a smaller, specified range
min-max normalization
z-score normalization
normalization by decimal scaling
Discretization: Concept hierarchy climbing
33. 33
Normalization
Min-max normalization: to [new_minA, new_maxA]
Ex. Let income range $12,000 to $98,000 normalized to [0.0,
1.0]. Then $73,000 is mapped to
Z-score normalization (μ: mean, σ: standard deviation):
Ex. Let μ = 54,000, σ = 16,000. Then
Normalization by decimal scaling
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34. 34
Discretization
Three types of attributes
Nominal—values from an unordered set, e.g., color, profession
Ordinal—values from an ordered set, e.g., military or academic
rank
Numeric—real numbers, e.g., integer or real numbers
Discretization: Divide the range of a continuous attribute into intervals
Interval labels can then be used to replace actual data values
Reduce data size by discretization
Supervised vs. unsupervised
Split (top-down) vs. merge (bottom-up)
Discretization can be performed recursively on an attribute
Prepare for further analysis, e.g., classification
35. 35
Data Discretization Methods
Typical methods: All the methods can be applied recursively
Binning
Top-down split, unsupervised
Histogram analysis
Top-down split, unsupervised
Clustering analysis (unsupervised, top-down split or
bottom-up merge)
Decision-tree analysis (supervised, top-down split)
Correlation (e.g., 2) analysis (unsupervised, bottom-up
merge)
36. 36
Simple Discretization: Binning
Equal-width (distance) partitioning
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
Divides the range into N intervals, each containing approximately
same number of samples
Good data scaling
Managing categorical attributes can be tricky
37. 37
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 equal-frequency (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
38. 38
Discretization by Classification &
Correlation Analysis
Classification (e.g., decision tree analysis)
Supervised: Given class labels, e.g., cancerous vs. benign
Using entropy to determine split point (discretization point)
Top-down, recursive split
Details to be covered in Chapter 7
Correlation analysis (e.g., Chi-merge: χ2-based discretization)
Supervised: use class information
Bottom-up merge: find the best neighboring intervals (those
having similar distributions of classes, i.e., low χ2 values) to merge
Merge performed recursively, until a predefined stopping condition
39. 39
Chapter 3: Data Preprocessing
Data Preprocessing: An Overview
Data Quality
Major Tasks in Data Preprocessing
Data Cleaning
Data Integration
Data Reduction
Data Transformation and Data Discretization
Summary
40. 40
Summary
Data quality: accuracy, completeness, consistency, timeliness,
believability, interpretability
Data cleaning: e.g. missing/noisy values, outliers
Data integration from multiple sources:
Entity identification problem
Remove redundancies
Detect inconsistencies
Data reduction
Dimensionality reduction
Numerosity reduction
Data compression
Data transformation and data discretization
Normalization
Concept hierarchy generation