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.
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.
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 "
This document discusses the importance of data preprocessing techniques for improving data quality. It outlines several key steps in data preprocessing: data cleaning, which detects and corrects errors and inconsistencies; data integration, which merges data from multiple sources; data reduction, which reduces data dimensions or volumes; and data transformation, which converts data into appropriate forms for analysis. Specific techniques discussed include missing value imputation, binning, smoothing, normalization, and discretization. The overall goal of data preprocessing is to prepare raw data for data mining and ensure high quality results.
This document discusses data preprocessing techniques. It begins by defining data and its key components - objects and attributes. It then provides an overview of common data preprocessing tasks including data cleaning (handling missing values, noise and outliers), data transformation (aggregation, type conversion, normalization), and data reduction (sampling, dimensionality reduction). Specific techniques are described for each task, such as binning values, imputation methods, and feature selection algorithms like ranking, forward selection and backward elimination. The document emphasizes that high quality data preprocessing is important and can improve predictive model performance.
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.
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, 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 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.
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 "
This document discusses the importance of data preprocessing techniques for improving data quality. It outlines several key steps in data preprocessing: data cleaning, which detects and corrects errors and inconsistencies; data integration, which merges data from multiple sources; data reduction, which reduces data dimensions or volumes; and data transformation, which converts data into appropriate forms for analysis. Specific techniques discussed include missing value imputation, binning, smoothing, normalization, and discretization. The overall goal of data preprocessing is to prepare raw data for data mining and ensure high quality results.
This document discusses data preprocessing techniques. It begins by defining data and its key components - objects and attributes. It then provides an overview of common data preprocessing tasks including data cleaning (handling missing values, noise and outliers), data transformation (aggregation, type conversion, normalization), and data reduction (sampling, dimensionality reduction). Specific techniques are described for each task, such as binning values, imputation methods, and feature selection algorithms like ranking, forward selection and backward elimination. The document emphasizes that high quality data preprocessing is important and can improve predictive model performance.
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.
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, 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 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, reduction, and discretization. The major tasks covered are filling in missing values, removing outliers, data integration, normalization, aggregation, dimensionality reduction, data cube aggregation, and discretization of continuous attributes. The goal of these techniques is to prepare raw data for analysis by handling issues like inconsistent, missing, or noisy data and reducing large volumes of data while retaining the most important information.
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.
This document provides an overview of key tasks in data preprocessing for knowledge discovery and data mining. It discusses why preprocessing is important, as real-world data is often dirty, noisy, incomplete and inconsistent. The major tasks covered are data cleaning, integration and transformation, reduction, discretization and concept hierarchy generation. Data cleaning involves techniques for handling missing data, noisy data and inconsistent data. Data reduction aims to reduce data volume while preserving analytical results. Methods discussed include binning, clustering, dimensionality reduction and sampling. Discretization converts continuous attributes to discrete intervals.
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.
The document discusses various techniques for data pre-processing. It begins by explaining why pre-processing is important for obtaining clean and consistent data needed for quality data mining results. It then covers topics such as data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques for handling missing values, outliers, and inconsistencies. Data integration combines data from multiple sources. Transformation techniques include normalization, aggregation, and generalization. Data reduction aims to reduce data volume while maintaining analytical quality. Discretization includes binning of continuous variables.
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.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
This document discusses data preprocessing techniques for data mining. It explains that real-world data is often dirty, containing issues like missing values, noise, and inconsistencies. Major tasks in data preprocessing include data cleaning, integration, transformation, reduction, and discretization. Data cleaning techniques are especially important and involve filling in missing values, identifying and handling outliers, resolving inconsistencies, and reducing redundancy from data integration. Other techniques discussed include binning data for smoothing noisy values and handling missing data through various imputation methods.
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 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 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 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. It describes how real-world data can be incomplete, noisy, or inconsistent. The main tasks in data preprocessing are cleaning the data by handling missing values and outliers, integrating and transforming the data by normalization and aggregation, reducing the data through dimensionality reduction, and discretizing numeric values. Specific techniques discussed include binning, clustering, regression, principle component analysis, and information-based discretization. The goal is to prepare raw data for further analysis.
Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
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.
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.
The document provides information on three programming languages: COBOL, LISP, and Python. COBOL was released in 1959 and was used for 80% of business transactions due to its reliability. LISP was the second high-level language created in 1958 and introduced innovations like garbage collection and recursion using linked lists. Python was developed in the 1990s and prioritizes readability through features like whitespace and a simple grammar.
The document discusses memory hierarchy and cache performance. It introduces the concept of memory hierarchy to get the best of fast and large memories. It then discusses different memory technologies like SRAM, DRAM and disk and their access times. It explains the basic concepts of direct mapped cache, cache hits, misses and different ways to reduce miss penalties like using multiple cache levels. Finally, it classifies cache misses into compulsory, capacity and conflict misses and how these are affected based on cache parameters.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, and discretization. The major tasks covered are filling in missing values, removing outliers, data integration, normalization, aggregation, dimensionality reduction, data cube aggregation, and discretization of continuous attributes. The goal of these techniques is to prepare raw data for analysis by handling issues like inconsistent, missing, or noisy data and reducing large volumes of data while retaining the most important information.
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.
This document provides an overview of key tasks in data preprocessing for knowledge discovery and data mining. It discusses why preprocessing is important, as real-world data is often dirty, noisy, incomplete and inconsistent. The major tasks covered are data cleaning, integration and transformation, reduction, discretization and concept hierarchy generation. Data cleaning involves techniques for handling missing data, noisy data and inconsistent data. Data reduction aims to reduce data volume while preserving analytical results. Methods discussed include binning, clustering, dimensionality reduction and sampling. Discretization converts continuous attributes to discrete intervals.
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.
The document discusses various techniques for data pre-processing. It begins by explaining why pre-processing is important for obtaining clean and consistent data needed for quality data mining results. It then covers topics such as data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques for handling missing values, outliers, and inconsistencies. Data integration combines data from multiple sources. Transformation techniques include normalization, aggregation, and generalization. Data reduction aims to reduce data volume while maintaining analytical quality. Discretization includes binning of continuous variables.
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.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
This document discusses data preprocessing techniques for data mining. It explains that real-world data is often dirty, containing issues like missing values, noise, and inconsistencies. Major tasks in data preprocessing include data cleaning, integration, transformation, reduction, and discretization. Data cleaning techniques are especially important and involve filling in missing values, identifying and handling outliers, resolving inconsistencies, and reducing redundancy from data integration. Other techniques discussed include binning data for smoothing noisy values and handling missing data through various imputation methods.
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 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 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 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. It describes how real-world data can be incomplete, noisy, or inconsistent. The main tasks in data preprocessing are cleaning the data by handling missing values and outliers, integrating and transforming the data by normalization and aggregation, reducing the data through dimensionality reduction, and discretizing numeric values. Specific techniques discussed include binning, clustering, regression, principle component analysis, and information-based discretization. The goal is to prepare raw data for further analysis.
Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
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.
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.
The document provides information on three programming languages: COBOL, LISP, and Python. COBOL was released in 1959 and was used for 80% of business transactions due to its reliability. LISP was the second high-level language created in 1958 and introduced innovations like garbage collection and recursion using linked lists. Python was developed in the 1990s and prioritizes readability through features like whitespace and a simple grammar.
The document discusses memory hierarchy and cache performance. It introduces the concept of memory hierarchy to get the best of fast and large memories. It then discusses different memory technologies like SRAM, DRAM and disk and their access times. It explains the basic concepts of direct mapped cache, cache hits, misses and different ways to reduce miss penalties like using multiple cache levels. Finally, it classifies cache misses into compulsory, capacity and conflict misses and how these are affected based on cache parameters.
This advertisement promotes the 2013 album "Comedown Machine" by the band The Strokes. It features silhouettes of the band members and resembles artwork from their previous albums released through RCA Records. The ad quotes praise for the album from Q Magazine, comparing it favorably to the band's debut album "Is This It". The ad helps promote the album's release since The Strokes provided little other promotion for "Comedown Machine". Its placement on the back cover of Mojo magazine exposes it to potential new readers in addition to the magazine's actual audience.
Game Theory was developed in the 1980s to analyze the feasibility of the US Strategic Defense Initiative ("Star Wars") program. It uses probabilities to model scenarios of defense ("Blue") against attack, attributing a 1/3 probability of Blue successfully defending. Game Theory was founded by mathematician John von Neumann, who was born in Budapest in 1903 and died in Washington D.C. in 1957, and focuses on modeling strategic decision-making involving multiple players.
UML (Unified Modeling Language) is a standardized language for modeling software systems using graphical notations. It allows project teams to communicate designs, explore potential solutions, and validate requirements and architecture. UML aims to provide a visual modeling language that is independent of programming languages and development processes. This lab introduces students to UML by explaining its history, goals, basic building blocks including model elements, relationships, and diagrams, and how it can be used to model systems from different perspectives.
The document discusses stacks and queues as data structures. It begins by defining a Node struct and List class to implement a linked list. It then provides an overview of stacks, including common stack operations like push and pop. Stacks follow LIFO order and can be implemented using arrays or linked lists. The document also discusses queues, which follow FIFO order. Key queue operations are enqueue and dequeue. Queues can also be implemented using arrays or linked lists. The document provides examples of implementing both stacks and queues as classes with the appropriate member functions.
This document presents a project report on developing a Campus News Feed application on the Android platform. It aims to allow students and faculty to easily stay informed about the latest campus news and updates by retrieving content from the college website. The application will connect to a backend database through an API. It will periodically check for new content and display it to users in the app. This provides an efficient notification system compared to traditional notice boards. The application is intended to facilitate information exchange within a college community and help users stay up-to-date on academic activities and developments.
The document discusses the key elements of the object model, including abstraction, encapsulation, modularity, and hierarchy. It explains that abstraction is one of the fundamental ways to cope with complexity in software design. Abstraction focuses on the essential characteristics of an object that distinguish it from other objects, from the perspective of the viewer. The object model provides a conceptual framework for object-oriented programming that is based on these elements.
This document provides an overview of XML Schema and the Stylus Studio application. It discusses how XML Schema defines the building blocks of an XML document and supports features like data types, namespaces, and extensibility. It then demonstrates how Stylus Studio can be used to graphically create XML Schemas by representing tables as complex elements, columns as sub-elements, and defining keys, primary keys, and foreign keys. The document shows the Stylus Studio interface and how to perform tasks like adding elements, setting data types, and validating the XML Schema.
1. Represents text documents as graph-of-words and extracts subgraph features through frequent subgraph mining to classify texts as a graph classification problem.
2. Uses gSpan algorithm to efficiently mine frequent subgraphs from the graph-of-words and selects the best minimum support threshold using the elbow method.
3. Evaluates on four datasets showing improved accuracy over bag-of-words models by capturing long-distance n-gram dependencies through subgraph features.
This document discusses Java lists and iterators. It provides information on:
1) The List interface and classes that implement it like ArrayList and LinkedList. It also discusses how to create and output List objects.
2) Common List methods like isEmpty(), size(), add(), get(), and the iterator() method.
3) Using iterators to traverse List elements, including the iterator interface methods and how to use a generic iterator in a for-each loop.
4) Differences between using an iterator versus a for-each loop, where an iterator is needed to remove elements from the List.
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 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.
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.
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.
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 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 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 involves several key steps:
1) Data cleaning to fill in missing values, identify and remove outliers, and resolve inconsistencies
2) Data integration to combine multiple data sources and resolve conflicts and redundancies
3) Data reduction techniques like discretization, dimensionality reduction, and aggregation to obtain a reduced representation of the data for mining and analysis.
The document discusses various techniques for data preparation and preprocessing for data warehousing and mining projects. It covers descriptive data summarization, data cleaning, integration and transformation, and reduction. The key aspects covered include handling missing data, resolving inconsistencies, reducing redundancy through integration, and reducing data volume through techniques like aggregation, generalization and discretization while maintaining analytical capabilities. Quality data preparation is emphasized as essential for obtaining quality mining results.
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 techniques for deterministic replay of multithreaded programs. It describes how recording shared memory ordering information can enable replay that reproduces data races and concurrency bugs. Specifically, it outlines using a directory-based approach to track read-write dependencies between threads and reduce the log size through transitive reduction of dependencies.
Multi-threaded processor architectures can improve parallelism at both the instruction-level and thread-level. Simultaneous multi-threading (SMT) allows multiple threads to issue and execute instructions simultaneously by dynamically sharing processor resources. SMT reduces underutilization of functional units and improves performance over multiprocessors. Multi-threaded designs are well-suited for digital signal processing applications that can benefit from parallel execution at multiple levels. Examples of multi-threaded real-time operating systems that support parallel DSP applications are discussed.
Recursion involves functions that call themselves during execution. This document discusses recursive functions like factorial, Fibonacci sequence, power, and printing patterns. Recursive functions have a base case that stops the recursion. The factorial function is defined recursively as n! = n * (n-1)!, and this recursive definition is evaluated step-by-step for fact(4). Recursive functions can also be used to define sequences like Fibonacci numbers.
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- Decision trees are a supervised learning technique used for classification problems. They work by recursively splitting a dataset based on the values of predictor variables and assigning class labels to the terminal nodes.
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Optimizing shared caches in chip multiprocessorsJames Wong
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The document proposes optimizing DRAM caches for latency rather than hit rate, as previous work had done. It presents the Alloy Cache design which avoids serializing tag and data accesses, keeping them in the same DRAM row for lower latency. Using a simple Memory Access Predictor (MAP), Alloy Cache can predict whether to use a higher-latency serial access model or lower-latency parallel model. With MAP, Alloy Cache outperforms previous designs like SRAM-tag caches with fewer optimizations that degrade performance. The document advocates revisiting DRAM cache designs given their different constraints compared to SRAM caches.
The document discusses the key elements of the object model, including abstraction, encapsulation, modularity, and hierarchy. It explains that abstraction is one of the fundamental ways to cope with complexity in software design. Abstraction focuses on the essential characteristics of an object that distinguish it from other objects, from the perspective of the viewer. The object model provides a conceptual framework for object-oriented programming that is based on these elements.
Abstract classes and interfaces allow for abstraction and polymorphism in object-oriented design. Abstract classes can contain both abstract and concrete methods, while interfaces only contain abstract methods. Abstract classes are used to provide a common definition for subclasses through inheritance, while interfaces define a contract for implementing classes to follow. Both support polymorphism by allowing subclasses/implementing classes to determine the specific implementation for abstract methods.
Object-oriented analysis and design (OOAD) emphasizes investigating requirements rather than solutions, and conceptual solutions that fulfill requirements rather than implementations. OOAD focuses on identifying domain concepts and defining software objects and how they collaborate. The Unified Process includes inception, elaboration, construction, and transition phases with iterations and milestones leading to final product releases.
The document discusses several programming paradigms including single process, multi process, and multi-core/multi-thread. It then covers topics such as processes, threads, synchronization, and liveness issues in concurrent programming including deadlock, starvation and livelock. Processes run independently while threads share data and scheduling. Synchronization prevents interference and inconsistencies between threads accessing shared data. Liveness issues can cause programs to halt indefinitely.
The document discusses abstract data types (ADTs), specifically queues. It defines a queue as a linear collection where elements are added to one end and removed from the other end, following a first-in, first-out (FIFO) approach. The key queue operations are enqueue, which adds an element, and dequeue, which removes the element that has been in the queue longest. Queues can be implemented using arrays or linked lists. Array implementations use head and tail pointers to track the start and end of the queue.
This document discusses inheritance in object-oriented programming. It explains that inheritance allows a subclass to inherit attributes and behaviors from a superclass, extending the superclass. This allows for code reuse and the establishment of class hierarchies. The document provides an example of a BankAccount superclass and SavingsAccount subclass, demonstrating how the subclass inherits methods like deposit() and withdraw() from the superclass while adding its own method, addInterest(). It also discusses polymorphism and access control as related concepts.
This document provides an overview of APIs, including what they are, why they are useful, common data formats like JSON and XML, RESTful API design principles, and how to consume and create APIs. It discusses API concepts like resources, HTTP verbs, caching, authentication, and error handling. It also provides examples of consuming APIs with tools like Postman and creating a simple API in Node.js.
Python is a multi-paradigm programming language that is object-oriented, imperative and functional. It is dynamically typed, with support for complex data types like lists and strings. Python code is commonly written and executed using the interactive development environment IDLE.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
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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
Required for both OLAP and Data Mining!
4. Why can Data be Incomplete?
Attributes of interest are not available (e.g.,
customer information for sales transaction data)
Data were not considered important at the time
of transactions, so they were not recorded!
Data not recorder because of misunderstanding
or malfunctions
Data may have been recorded and later deleted!
Missing/unknown values for some data
5. Why can Data be Noisy/Inconsistent?
Faulty instruments for data collection
Human or computer errors
Errors in data transmission
Technology limitations (e.g., sensor data come at
a faster rate than they can be processed)
Inconsistencies in naming conventions or data
codes (e.g., 2/5/2002 could be 2 May 2002 or 5
Feb 2002)
Duplicate tuples, which were received twice
should also be removed
6. 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
outliers=exceptions!
8. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
9. Data Cleaning
Data cleaning tasks
Fill in missing values
Identify outliers and smooth out noisy data
Correct inconsistent data
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 attribute mean for all samples belonging to the same
class to fill in the missing value: smarter
Use the most probable value to fill in the missing value:
inference-based such as Bayesian formula or decision tree
11. How to Handle Missing Data?
Age Income Religion Gender
23 24,200 Muslim M
39 ? Christian F
45 45,390 ? F
Fill missing values using aggregate functions (e.g., average) or
probabilistic estimates on global value distribution
E.g., put the average income here, or put the most probable income
based on the fact that the person is 39 years old
E.g., put the most frequent religion here
12. Noisy Data
Noise: random error or variance in a measured
variable
Incorrect attribute values may exist 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
13. How to Handle Noisy Data?
Smoothing techniques
Binning method:
first sort data and partition into (equi-depth) bins
then one can smooth by bin means, smooth by bin
median, smooth by bin boundaries, etc.
Clustering
detect and remove outliers
Combined computer and human inspection
computer detects suspicious values, which are then
checked by humans
Regression
smooth by fitting the data into regression functions
Use Concept hierarchies
use concept hierarchies, e.g., price value -> “expensive”
14. 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 – good handing of skewed data
19. Inconsistent Data
Inconsistent data are handled by:
Manual correction (expensive and tedious)
Use routines designed to detect inconsistencies
and manually correct them. E.g., the routine may
use the check global constraints (age>10) or
functional dependencies
Other inconsistencies (e.g., between names of
the same attribute) can be corrected during the
data integration process
20. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
21. Data Integration
Data integration:
combines data from multiple sources into a coherent store
Schema integration
integrate metadata from different sources
metadata: data about the data (i.e., data descriptors)
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 (e.g., J.D.Smith and Jonh
Smith may refer to the same person)
possible reasons: different representations, different
scales, e.g., metric vs. British units (inches vs. cm)
22. Handling Redundant
Data in Data Integration
Redundant data occur often when integration of
multiple databases
The same attribute may have different names in different
databases
One attribute may be a “derived” attribute in another
table, e.g., annual revenue
Redundant data may be able to be detected by
correlation analysis
Careful integration of the data from multiple
sources may help reduce/avoid redundancies and
inconsistencies and improve mining speed and
quality
23. 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
Attribute/feature construction
New attributes constructed from the given ones
24. Normalization: Why normalization?
Speeds-up some learning techniques (ex.
neural networks)
Helps prevent attributes with large ranges
outweigh ones with small ranges
Example:
income has range 3000-200000
age has range 10-80
gender has domain M/F
25. Data Transformation: Normalization
min-max normalization
e.g. convert age=30 to range 0-1, when
min=10,max=80. new_age=(30-10)/(80-10)=2/7
z-score normalization
normalization by decimal scaling
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26. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
27. 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
Data compression
Numerosity reduction
Discretization and concept hierarchy generation
28. Data Cube Aggregation
The lowest level of a data cube
the aggregated data for an individual entity of interest
e.g., a customer in a phone calling data warehouse.
Multiple levels of aggregation in data cubes
Further reduce the size of data to deal with
Reference appropriate levels
Use the smallest representation which is enough to solve
the task
Queries regarding aggregated information should be
answered using data cube, when possible
29. Dimensionality Reduction
Feature selection (i.e., attribute subset selection):
Select a minimum set of features such that the probability
distribution of different classes given the values for those
features is as close as possible to the original distribution
given the values of all features
reduce # of patterns in the patterns, easier to understand
Heuristic methods (due to exponential # of
choices):
step-wise forward selection
step-wise backward elimination
combining forward selection and backward elimination
decision-tree induction
30. Heuristic Feature Selection Methods
There are 2d
possible sub-features of d features
Several heuristic feature selection methods:
Best single features under the feature independence
assumption: choose by significance tests.
Best step-wise feature selection:
The best single-feature is picked first
Then next best feature condition to the first, ...
Step-wise feature elimination:
Repeatedly eliminate the worst feature
Best combined feature selection and elimination:
Optimal branch and bound:
Use feature elimination and backtracking
31. Example of Decision Tree Induction
Initial attribute set:
{A1, A2, A3, A4, A5, A6}
A4 ?
A1? A6?
Class 1 Class 2 Class 1 Class 2
> Reduced attribute set: {A1, A4, A6}
33. Given N data vectors from k-dimensions, find c
<= k orthogonal vectors that can be best used
to represent data
The original data set is reduced to one consisting of N
data vectors on c principal components (reduced
dimensions)
Each data vector is a linear combination of the c
principal component vectors
Works for numeric data only
Used when the number of dimensions is large
Principal Component Analysis or
Karhuren-Loeve (K-L) method
34. X1
X2
Y1
Y2
Principal Component Analysis
X1, X2: original axes (attributes)
Y1,Y2: principal components
significant component
(high variance)
Order principal components by significance and eliminate weaker ones
35. Numerosity Reduction:
Reduce the volume of data
Parametric methods
Assume the data fits some model, estimate model
parameters, store only the parameters, and discard the
data (except possible outliers)
Log-linear models: obtain value at a point in m-D
space as the product on appropriate marginal
subspaces
Non-parametric methods
Do not assume models
Major families: histograms, clustering, sampling
36. Histograms
A popular data
reduction technique
Divide data into
buckets and store
average (or 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 30000 50000 70000 90000
37. Histogram types
Equal-width histograms:
It divides the range into N intervals of equal size
Equal-depth (frequency) partitioning:
It divides the range into N intervals, each containing
approximately same number of samples
V-optimal:
It considers all histogram types for a given number of
buckets and chooses the one with the least variance.
MaxDiff:
After sorting the data to be approximated, it defines the
borders of the buckets at points where the adjacent
values have the maximum difference
Example: split 1,1,4,5,5,7,9,14,16,18,27,30,30,32 to three
buckets
MaxDiff 27-18 and 14-9
Histograms
38. Clustering
Partitions data set into clusters, and models it by
one representative from each cluster
Can be very effective if data is clustered but not
if data is “smeared”
There are many choices of clustering definitions
and clustering algorithms, further detailed in
Chapter 7
40. Hierarchical Reduction
Use multi-resolution structure with different
degrees of reduction
Hierarchical clustering is often performed but tends
to define partitions of data sets rather than
“clusters”
Parametric methods are usually not amenable to
hierarchical representation
Hierarchical aggregation
An index tree hierarchically divides a data set into
partitions by value range of some attributes
Each partition can be considered as a bucket
Thus an index tree with aggregates stored at each node is
a hierarchical histogram
41. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
42. 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
why?
Some classification algorithms only accept
categorical attributes.
Reduce data size by discretization
Prepare for further analysis
43. 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).
44. Discretization and concept hierarchy
generation for numeric data
Binning/Smoothing (see sections before)
Histogram analysis (see sections before)
Clustering analysis (see sections before)
Entropy-based discretization
Segmentation by natural partitioning
45. Entropy-Based Discretization
Given a set of samples S, if S is partitioned into
two intervals S1 and S2 using boundary T, the
information gain I(S,T) after partitioning is
The boundary that maximizes the information gain
over all possible boundaries is selected as a binary
discretization.
The process is recursively applied to partitions
obtained until some stopping criterion is met, e.g.,
Experiments show that it may reduce data size
and improve classification accuracy
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46. Segmentation by natural partitioning
The 3-4-5 rule can be used to segment numerical data into
relatively uniform, “natural” intervals.
* If an interval covers 3, 6, 7 or 9 distinct values at the most
significant digit, partition the range into 3 equiwidth intervals
for 3,6,9 or 2-3-2 for 7
* If it covers 2, 4, or 8 distinct values at the most significant
digit, partition the range into 4 equiwidth intervals
* If it covers 1, 5, or 10 distinct values at the most significant
digit, partition the range into 5 equiwidth intervals
Users often like to see numerical ranges partitioned into
relatively uniform, easy-to-read intervals that appear intuitive
or “natural”. E.g., [50-60] better than [51.223-60.812]
The rule can be recursively applied for the resulting intervals
47. Concept hierarchy generation for
categorical data
Categorical attributes: finite, possibly large domain, with no
ordering among the values
Example: item type
Specification of a partial ordering of attributes explicitly at
the schema level by users or experts
Example: location is split by domain experts to
street<city<state<country
Specification of a portion of a hierarchy by explicit data
grouping
Specification of a set of attributes, but not of their partial
ordering
Specification of only a partial set of attributes
48. Specification of a set of attributes
Concept hierarchy can be automatically
generated based on the number of distinct
values per attribute in the given attribute set.
The attribute with the most distinct values is
placed at the lowest level of the hierarchy.
country
province_or_ state
city
street
15 distinct values
65 distinct
values
3567 distinct values
674,339 distinct values