This document discusses various techniques for data preprocessing, including data integration, transformation, reduction, and discretization. It covers topics such as schema integration, handling redundant data, data normalization, dimensionality reduction, data cube aggregation, sampling, and entropy-based discretization. The goal of these techniques is to prepare raw data for knowledge discovery and data mining tasks by cleaning, transforming, and reducing the data into a suitable structure.
Data extraction, cleanup & transformation tools 29.1.16Dhilsath Fathima
Data preprocessing is an important step in the data mining process that involves transforming raw data into an understandable format. It includes tasks like data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers, handles missing data and resolves inconsistencies. Data integration combines data from multiple sources. Transformation includes normalization and aggregation. Reduction techniques like binning, clustering, and sampling reduce data volume while maintaining analytical quality. Dimensionality reduction selects a minimum set of important features.
This document discusses various techniques for data preprocessing which is an important step in the knowledge discovery process. It covers why preprocessing is needed due to issues with real-world data being dirty, noisy, incomplete etc. The major tasks covered are data cleaning, integration, transformation and reduction. Specific techniques discussed include data cleaning methods like handling missing values, noisy data, and inconsistencies. Data integration addresses combining multiple sources and resolving conflicts. Transformation techniques include normalization, aggregation, and discretization. Data reduction aims to reduce volume while maintaining analytical quality using methods like cube aggregation, dimensionality reduction, and data compression.
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, transformation, reduction, discretization, and generating concept hierarchies. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and generalization. The goal of data reduction is to obtain a smaller yet similar representation, using techniques like cube aggregation, dimensionality reduction, sampling, and discretization.
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, transformation, reduction, discretization, and generating concept hierarchies. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and generalization. The goal of data reduction is to obtain a smaller yet similar representation, using techniques like cube aggregation, dimensionality reduction, sampling, and discretization.
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, transformation, reduction, discretization, and generating concept hierarchies. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and generalization. The goal of data reduction is to obtain a smaller yet similar representation, using techniques like cube aggregation, dimensionality reduction, sampling, and discretization.
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 IoT applications. It covers why preprocessing is important, as real-world data can be dirty, incomplete, noisy, or inconsistent. The major tasks covered are data cleaning, integration, transformation, and reduction. Data cleaning involves filling in missing values, identifying outliers, and resolving inconsistencies. Data integration combines multiple data sources. Data transformation techniques include normalization, aggregation, and discretization. Data reduction obtains a reduced representation of data through techniques like binning, clustering, dimensionality reduction, and sampling.
This document discusses data preprocessing techniques. It explains that real-world data is often dirty, incomplete, noisy, and inconsistent. The main tasks in data preprocessing are data cleaning, integration, reduction, and transformation. Data cleaning involves filling in missing values, smoothing noisy data, and resolving inconsistencies. Data integration combines data from multiple sources. Data reduction techniques include dimensionality reduction, numerosity reduction, and data compression. Data transformation includes normalization, aggregation, and discretization.
Data extraction, cleanup & transformation tools 29.1.16Dhilsath Fathima
Data preprocessing is an important step in the data mining process that involves transforming raw data into an understandable format. It includes tasks like data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers, handles missing data and resolves inconsistencies. Data integration combines data from multiple sources. Transformation includes normalization and aggregation. Reduction techniques like binning, clustering, and sampling reduce data volume while maintaining analytical quality. Dimensionality reduction selects a minimum set of important features.
This document discusses various techniques for data preprocessing which is an important step in the knowledge discovery process. It covers why preprocessing is needed due to issues with real-world data being dirty, noisy, incomplete etc. The major tasks covered are data cleaning, integration, transformation and reduction. Specific techniques discussed include data cleaning methods like handling missing values, noisy data, and inconsistencies. Data integration addresses combining multiple sources and resolving conflicts. Transformation techniques include normalization, aggregation, and discretization. Data reduction aims to reduce volume while maintaining analytical quality using methods like cube aggregation, dimensionality reduction, and data compression.
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, transformation, reduction, discretization, and generating concept hierarchies. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and generalization. The goal of data reduction is to obtain a smaller yet similar representation, using techniques like cube aggregation, dimensionality reduction, sampling, and discretization.
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, transformation, reduction, discretization, and generating concept hierarchies. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and generalization. The goal of data reduction is to obtain a smaller yet similar representation, using techniques like cube aggregation, dimensionality reduction, sampling, and discretization.
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, transformation, reduction, discretization, and generating concept hierarchies. Data cleaning involves techniques for handling missing data, noisy data, and inconsistencies. Data integration combines multiple data sources. Data transformation includes normalization, aggregation, and generalization. The goal of data reduction is to obtain a smaller yet similar representation, using techniques like cube aggregation, dimensionality reduction, sampling, and discretization.
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 IoT applications. It covers why preprocessing is important, as real-world data can be dirty, incomplete, noisy, or inconsistent. The major tasks covered are data cleaning, integration, transformation, and reduction. Data cleaning involves filling in missing values, identifying outliers, and resolving inconsistencies. Data integration combines multiple data sources. Data transformation techniques include normalization, aggregation, and discretization. Data reduction obtains a reduced representation of data through techniques like binning, clustering, dimensionality reduction, and sampling.
This document discusses data preprocessing techniques. It explains that real-world data is often dirty, incomplete, noisy, and inconsistent. The main tasks in data preprocessing are data cleaning, integration, reduction, and transformation. Data cleaning involves filling in missing values, smoothing noisy data, and resolving inconsistencies. Data integration combines data from multiple sources. Data reduction techniques include dimensionality reduction, numerosity reduction, and data compression. Data transformation includes normalization, aggregation, and discretization.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Data cleaning involves handling missing data, noisy data, and inconsistent data through techniques like filling in missing values, identifying outliers, and correcting errors. Data integration combines data from multiple sources and resolves issues like redundant or conflicting data. Data transformation techniques normalize data scales and construct new attributes. Data reduction methods like sampling, clustering, and histograms reduce data volume while maintaining analytical quality. Discretization converts continuous attributes to categorical bins. Concept hierarchies generalize data by grouping values into higher-level concepts.
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/
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 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.
Data pre-processing involves cleaning raw data by filling in missing values, removing noise, and resolving inconsistencies. It also includes integrating, transforming, and reducing data through techniques like normalization, aggregation, dimensionality reduction, and discretization. The goal of data pre-processing is to convert raw data into a clean, organized format suitable for modeling and analysis tasks like data mining and machine learning.
This document discusses various techniques for data reduction, including dimensionality reduction, sampling, binning/cardinality reduction, and parametric methods like regression and log-linear models. Dimensionality reduction techniques aim to reduce the number of attributes/variables, like principal component analysis (PCA) and feature selection. Sampling reduces the number of data instances. Binning and cardinality reduction transform data into a reduced representation. Parametric methods model the data and store only the parameters.
This document discusses various techniques for data pre-processing and data reduction. It covers data cleaning techniques like handling missing data, noisy data, and data transformation. It also discusses data integration techniques like entity identification, redundancy analysis, and detecting tuple duplication. For data reduction, it discusses dimensionality reduction methods like wavelet transforms and principal component analysis. It also covers numerosity reduction techniques like regression models, histograms, clustering, sampling, and data cube aggregation. The goal of these techniques is to prepare raw data for further analysis and handle issues like inconsistencies, missing values, and reduce data size.
Data is often incomplete, noisy, and inconsistent which can negatively impact mining results. Effective data cleaning is needed to fill in missing values, identify and remove outliers, and resolve inconsistencies. Other important tasks include data integration, transformation, reduction, and discretization to prepare the data for mining and obtain reduced representation that produces similar analytical results. Proper data preparation is essential for high quality knowledge discovery.
Data preprocessing is the process of preparing raw data for analysis by cleaning it, transforming it, and reducing it. The key steps in data preprocessing include data cleaning to handle missing values, outliers, and noise; data transformation techniques like normalization, discretization, and feature extraction; and data reduction methods like dimensionality reduction and sampling. Preprocessing ensures the data is consistent, accurate and suitable for building machine learning models.
This document discusses data preprocessing techniques. It begins by explaining why preprocessing is important due to real-world data often being dirty, incomplete, noisy, or inconsistent. The main tasks of preprocessing are then outlined as data cleaning, integration, reduction, and transformation. Specific techniques for handling missing data, noisy data, and data integration are then described. Methods for data reduction through dimensionality reduction, numerosity reduction, and discretization are also summarized.
Here are the steps to find the first and third quartiles for this data:
1) List the values in ascending order: 59, 60, 62, 64, 66, 67, 69, 70, 72
2) The number of observations is 9. To find the first quartile (Q1), we take the value at position ⌊(n+1)/4⌋ = ⌊(9+1)/4⌋ = 3.
The third value is 62.
3) To find the third quartile (Q3), we take the value at position ⌊3(n+1)/4⌋ = ⌊3(9+1)/4
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for further analysis. The key tasks in data preprocessing are data cleaning to handle missing values, noise, outliers and inconsistencies; data integration of multiple data sources; data transformation through normalization, aggregation, and attribute construction; data reduction to reduce data size through methods like binning, clustering and sampling; and data discretization to reduce continuous attribute values into intervals. Descriptive statistics like mean, median and histograms can help identify noise and outliers during data cleaning.
Dimensionality reduction techniques transform high-dimensional data into a lower-dimensional representation while retaining important information. Principal component analysis (PCA) is a common linear technique that projects data along directions of maximum variance to obtain principal components as new uncorrelated variables. It works by computing the covariance matrix of standardized data to identify correlations, then computes the eigenvalues and eigenvectors of the covariance matrix to identify the principal components that capture the most information with fewer dimensions.
Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
Foundations of Machine Learning - StampedeCon AI Summit 2017StampedeCon
This presentation will cover all aspects of modeling, from preparing data, training and evaluating the results. There will be descriptions of the mainline ML methods including, neural nets, SVM, boosting, bagging, trees, forests, and deep learning. common problems of overfitting and dimensionality will be covered with discussion of modeling best practices. Other topics will include field standardization, encoding categorical variables, feature creation and selection. It will be a soup-to-nuts overview of all the necessary procedures for building state-of-the art predictive models.
The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.
Practical eLearning Makeovers for EveryoneBianca Woods
Welcome to Practical eLearning Makeovers for Everyone. In this presentation, we’ll take a look at a bunch of easy-to-use visual design tips and tricks. And we’ll do this by using them to spruce up some eLearning screens that are in dire need of a new look.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Data cleaning involves handling missing data, noisy data, and inconsistent data through techniques like filling in missing values, identifying outliers, and correcting errors. Data integration combines data from multiple sources and resolves issues like redundant or conflicting data. Data transformation techniques normalize data scales and construct new attributes. Data reduction methods like sampling, clustering, and histograms reduce data volume while maintaining analytical quality. Discretization converts continuous attributes to categorical bins. Concept hierarchies generalize data by grouping values into higher-level concepts.
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/
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 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.
Data pre-processing involves cleaning raw data by filling in missing values, removing noise, and resolving inconsistencies. It also includes integrating, transforming, and reducing data through techniques like normalization, aggregation, dimensionality reduction, and discretization. The goal of data pre-processing is to convert raw data into a clean, organized format suitable for modeling and analysis tasks like data mining and machine learning.
This document discusses various techniques for data reduction, including dimensionality reduction, sampling, binning/cardinality reduction, and parametric methods like regression and log-linear models. Dimensionality reduction techniques aim to reduce the number of attributes/variables, like principal component analysis (PCA) and feature selection. Sampling reduces the number of data instances. Binning and cardinality reduction transform data into a reduced representation. Parametric methods model the data and store only the parameters.
This document discusses various techniques for data pre-processing and data reduction. It covers data cleaning techniques like handling missing data, noisy data, and data transformation. It also discusses data integration techniques like entity identification, redundancy analysis, and detecting tuple duplication. For data reduction, it discusses dimensionality reduction methods like wavelet transforms and principal component analysis. It also covers numerosity reduction techniques like regression models, histograms, clustering, sampling, and data cube aggregation. The goal of these techniques is to prepare raw data for further analysis and handle issues like inconsistencies, missing values, and reduce data size.
Data is often incomplete, noisy, and inconsistent which can negatively impact mining results. Effective data cleaning is needed to fill in missing values, identify and remove outliers, and resolve inconsistencies. Other important tasks include data integration, transformation, reduction, and discretization to prepare the data for mining and obtain reduced representation that produces similar analytical results. Proper data preparation is essential for high quality knowledge discovery.
Data preprocessing is the process of preparing raw data for analysis by cleaning it, transforming it, and reducing it. The key steps in data preprocessing include data cleaning to handle missing values, outliers, and noise; data transformation techniques like normalization, discretization, and feature extraction; and data reduction methods like dimensionality reduction and sampling. Preprocessing ensures the data is consistent, accurate and suitable for building machine learning models.
This document discusses data preprocessing techniques. It begins by explaining why preprocessing is important due to real-world data often being dirty, incomplete, noisy, or inconsistent. The main tasks of preprocessing are then outlined as data cleaning, integration, reduction, and transformation. Specific techniques for handling missing data, noisy data, and data integration are then described. Methods for data reduction through dimensionality reduction, numerosity reduction, and discretization are also summarized.
Here are the steps to find the first and third quartiles for this data:
1) List the values in ascending order: 59, 60, 62, 64, 66, 67, 69, 70, 72
2) The number of observations is 9. To find the first quartile (Q1), we take the value at position ⌊(n+1)/4⌋ = ⌊(9+1)/4⌋ = 3.
The third value is 62.
3) To find the third quartile (Q3), we take the value at position ⌊3(n+1)/4⌋ = ⌊3(9+1)/4
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for further analysis. The key tasks in data preprocessing are data cleaning to handle missing values, noise, outliers and inconsistencies; data integration of multiple data sources; data transformation through normalization, aggregation, and attribute construction; data reduction to reduce data size through methods like binning, clustering and sampling; and data discretization to reduce continuous attribute values into intervals. Descriptive statistics like mean, median and histograms can help identify noise and outliers during data cleaning.
Dimensionality reduction techniques transform high-dimensional data into a lower-dimensional representation while retaining important information. Principal component analysis (PCA) is a common linear technique that projects data along directions of maximum variance to obtain principal components as new uncorrelated variables. It works by computing the covariance matrix of standardized data to identify correlations, then computes the eigenvalues and eigenvectors of the covariance matrix to identify the principal components that capture the most information with fewer dimensions.
Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
Foundations of Machine Learning - StampedeCon AI Summit 2017StampedeCon
This presentation will cover all aspects of modeling, from preparing data, training and evaluating the results. There will be descriptions of the mainline ML methods including, neural nets, SVM, boosting, bagging, trees, forests, and deep learning. common problems of overfitting and dimensionality will be covered with discussion of modeling best practices. Other topics will include field standardization, encoding categorical variables, feature creation and selection. It will be a soup-to-nuts overview of all the necessary procedures for building state-of-the art predictive models.
The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.
Practical eLearning Makeovers for EveryoneBianca Woods
Welcome to Practical eLearning Makeovers for Everyone. In this presentation, we’ll take a look at a bunch of easy-to-use visual design tips and tricks. And we’ll do this by using them to spruce up some eLearning screens that are in dire need of a new look.
ARENA - Young adults in the workplace (Knight Moves).pdfKnight Moves
Presentations of Bavo Raeymaekers (Project lead youth unemployment at the City of Antwerp), Suzan Martens (Service designer at Knight Moves) and Adriaan De Keersmaeker (Community manager at Talk to C)
during the 'Arena • Young adults in the workplace' conference hosted by Knight Moves.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
Explore the essential graphic design tools and software that can elevate your creative projects. Discover industry favorites and innovative solutions for stunning design results.
1. CIS664-Knowledge Discovery
and Data Mining
Vasileios Megalooikonomou
Dept. of Computer and Information Sciences
Temple University
Data Preprocessing
(based on notes by Jiawei Han and Micheline Kamber)
2. Data Integration
• Data integration:
– combines data from multiple sources into a coherent store
• Schema integration
– integrate metadata from different sources
– Entity identification problem: identify real world entities from
multiple data sources, e.g., A.cust-id B.cust-#
• Detecting and resolving data value conflicts
– for the same real world entity, attribute values from different
sources are different
– possible reasons: different representations, different scales,
e.g., metric vs. British units, different currency
3. Handling Redundant Data in
Data Integration
• Redundant data occur often when integrating multiple DBs
– 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 correlational
analysis
• Careful integration can help reduce/avoid redundancies and
inconsistencies and improve mining speed and quality
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4. Data Transformation
• Smoothing: remove noise from data (binning,
clustering, regression)
• 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
5. Data Transformation: Normalization
• min-max normalization
• z-score normalization
• normalization by decimal scaling
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Particularly useful for classification (NNs, distance measurements,
nn classification, etc)
6. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
7. Data Reduction
• Problem:
Data Warehouse may store terabytes of data:
Complex data analysis/mining may take a very
long time to run on the complete data set
• Solution?
– Data reduction…
8. •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
Data Reduction
9. Data Cube Aggregation
• Multiple levels of aggregation in data cubes
– Further reduce the size of data to deal with
• Reference appropriate levels
– Use the smallest representation capable to solve the
task
• Queries regarding aggregated information should
be answered using data cube, when possible
10. Dimensionality Reduction
• Problem: 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
– Nice side-effect: reduces # of attributes in the discovered patterns
(which are now easier to understand)
• Solution: Heuristic methods (due to exponential # of
choices) usually greedy:
– step-wise forward selection
– step-wise backward elimination
– combining forward selection and backward elimination
– decision-tree induction
11. 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}
nonleaf nodes: tests
branches: outcomes of tests
leaf nodes: class prediction
12. Data Compression
• String compression
– There are extensive theories and well-tuned algorithms
– Typically lossless
– But only limited manipulation is possible without expansion
• Audio/video, image compression
– Typically lossy compression, with progressive refinement
– Sometimes small fragments of signal can be reconstructed
without reconstructing the whole
• Time sequence is not audio
– Typically short and vary slowly with time
14. Wavelet Transforms
• Discrete wavelet transform (DWT):
linear signal processing
• Compressed approximation: store only a small fraction of
the strongest of the wavelet coefficients
• Similar to discrete Fourier transform (DFT), but better lossy
compression, localized in space (conserves local details)
• Method (hierarchical pyramid algorithm):
– Length, L, must be an integer power of 2 (padding with 0s, when necessary)
– Each transform has 2 functions:
• smoothing (e.g., sum, weighted avg.), weighted difference
– Applies to pairs of data, resulting in two sets of data of length L/2
– Applies the two functions recursively, until reaches the desired length
Haar2 Daubechie4
15. • 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 (projected) 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 ordered and unordered attributes
• Used when the number of dimensions is large
Principal Component Analysis (PCA)
Karhunen-Loeve (K-L) method
16. X1
X2
Y1
Y2
Principal Component Analysis
•The principal components (new set of axes) give important information about variance.
•Using the strongest components one can reconstruct a good approximation of the
original signal.
17. Numerosity Reduction
• Parametric methods
– Assume the data fits some model, estimate model
parameters, store only the parameters, and discard the data
(except possible outliers)
– E.g.: 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
18. Regression and Log-Linear Models
• Linear regression: Data are modeled to fit a straight
line:
– Often uses the least-square method to fit the line
• Multiple regression: allows a response variable y to
be modeled as a linear function of multidimensional
feature vector (predictor variables)
• Log-linear model: approximates discrete
multidimensional joint probability distributions
19. • Linear regression: Y = + X
– Two parameters , and specify the line and are to be
estimated by using the data at hand.
– using the least squares criterion to the known values of Y1,
Y2, …, X1, X2, ….
• Multiple regression: Y = b0 + b1 X1 + b2 X2.
– Many nonlinear functions can be transformed into the above.
• Log-linear models:
– The multi-way table of joint probabilities is approximated by
a product of lower-order tables.
– Probability: p(a, b, c, d) = ab acad bcd
Regression Analysis and Log-Linear Models
20. Histograms
• Approximate data
distributions
• Divide data into buckets
and store average (sum) for
each bucket
• A bucket represents an
attribute-value/frequency
pair
• 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
21. Clustering
• Partition data set into clusters, and store cluster representation only
• Quality of clusters measured by their diameter (max distance
between any two objects in the cluster) or centroid distance (avg.
distance of each cluster object from its centroid)
• Can be very effective if data is clustered but not if data is “smeared”
• Can have hierarchical clustering (possibly stored in multi-
dimensional index tree structures (B+-tree, R-tree, quad-tree, etc))
• There are many choices of clustering definitions and clustering
algorithms (further details later)
22. Sampling
• Allow a mining algorithm to run in complexity that is
potentially sub-linear to the size of the data
• Cost of sampling: proportional to the size of the sample,
increases linearly with the number of dimensions
• Choose a representative subset of the data
– Simple random sampling may have very poor performance in the
presence of skew
• Develop adaptive sampling methods
– Stratified sampling:
• Approximate the percentage of each class (or subpopulation of
interest) in the overall database
• Used in conjunction with skewed data
• Sampling may not reduce database I/Os (page at a time).
• Sampling: natural choice for progressive refinement of a
reduced data set.
25. 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
26. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
27. Discretization/Quantization
• Three types of attributes:
– Nominal — values from an unordered set
– Ordinal — values from an ordered set
– Continuous — real numbers
• Discretization/Quantization:
divide the range of a continuous attribute into intervals
– Some classification algorithms only accept categorical
attributes.
– Reduce data size by discretization
– Prepare for further analysis
x1 x2 x3 x4 x5
y1 y2 y3 y4 y5 y6
28. Discretization and Concept Hierarchy
• 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).
29. Discretization and concept hierarchy
generation for numeric data
• Hierarchical and recursive decomposition using:
– Binning (data smoothing)
– Histogram analysis (numerosity reduction)
– Clustering analysis (numerosity reduction)
• Entropy-based discretization
• Segmentation by natural partitioning
30. Entropy-Based Discretization
• Given a set of samples S, if S is partitioned into two intervals S1 and
S2 using threshold T on the value of attribute A, the information
gain resulting from the partitioning is:
where the entropy function E for a given set is calculated based on
the class distribution of the samples in the set. Given m classes the
entropy of S1 is:
where pi is the probability of class i in S1.
• The threshold that maximizes the information gain over all possible
thresholds 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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31. Segmentation by natural partitioning
• 3-4-5 rule can be used to segment numeric data into
relatively uniform, “natural” intervals.
• It partitions a given range into 3,4, or 5 equiwidth
intervals recursively level-by-level based on the value
range of the most significant digit.
* If an interval covers 3, 6, 7 or 9 distinct values at the most
significant digit, partition the range into 3 equi-width intervals
* If it covers 2, 4, or 8 distinct values at the most significant digit,
partition the range into 4 intervals
* If it covers 1, 5, or 10 distinct values at the most significant digit,
partition the range into 5 intervals
33. Concept hierarchy generation for
categorical data
• Categorical data: no ordering among values
• Specification of a partial ordering of attributes
explicitly at the schema level by users or experts
• 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
34. Concept hierarchy generation w/o data
semantics - 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 (limitations?)
country
province_or_ state
city
street
15 distinct values
65 distinct values
3567 distinct values
674,339 distinct values
35. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
36. Summary
• Data preparation is a big issue for both warehousing
and mining
• Data preparation includes
– Data cleaning and data integration
– Data reduction and feature selection
– Discretization
• A lot a methods have been developed but still an
active area of research