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 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 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 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.
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.
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 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.
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 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 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 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 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.
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.
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 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.
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 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 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 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.
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 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 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 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 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.
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.
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 is crucial for data mining and involves transforming raw data into a clean, consistent format. It includes data cleaning to handle incomplete, noisy, and inconsistent data; data integration of multiple sources; and data transformation through normalization, aggregation, and reduction. The major tasks in data preprocessing are data cleaning, integration, transformation, reduction, and discretization of attributes.
The document discusses data cleaning which aims to fill in missing values, smooth noisy data, identify outliers, and resolve inconsistencies in data. It describes various techniques for handling dirty data issues like missing values, noisy data, and inconsistencies. The goal of data cleaning is to produce high quality data that can be effectively mined to generate meaningful and useful results.
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.
This document discusses module 3 on data pre-processing. It begins with an overview of data quality issues like accuracy, completeness, consistency and timeliness. The major tasks in data pre-processing are then summarized as data cleaning, integration, reduction, and transformation. Data cleaning techniques like handling missing values, noisy data and outliers are covered. The need for feature engineering and dimensionality reduction due to the curse of dimensionality is also highlighted. Finally, techniques for data integration, reduction and discretization are briefly introduced.
Data pre-processing is an important step that includes cleaning, normalization, transformation, feature extraction and selection to produce the final training set. It addresses real-world data issues like incompleteness, noise, and inconsistencies. The main tasks are data cleaning, integration, transformation, reduction, and discretization. Data cleaning fills in missing values and identifies/removes outliers. Data is normalized, aggregated, and generalized. Reduction decreases attributes and tuples through binning, clustering, sampling and other techniques. Data mining tools include traditional programs, dashboards to monitor business performance, and text mining tools to extract structured and unstructured data.
This document discusses various techniques for data processing including data cleaning, integration, transformation, reduction, and similarity/dissimilarity measures. It describes common types of dirty or incomplete data like missing values, noisy data, and inconsistent data. It also discusses techniques for handling different types of dirty data, such as filling in missing values, smoothing noisy data, and resolving inconsistencies. Major tasks in data processing include data cleaning, integration, transformation, and reduction. Specific techniques discussed include binning, clustering, regression, normalization, aggregation, and dimensionality reduction. The document also provides details on various similarity and dissimilarity measures that can be used to calculate the proximity between data objects.
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.
This document discusses data preprocessing techniques. It explains that data is often incomplete, noisy, or inconsistent when collected from the real world. Common preprocessing steps include data cleaning to handle these issues, data integration and transformation to combine multiple data sources, and data reduction to reduce the volume of data for analysis while maintaining analytical results. Specific techniques covered include filling in missing values, identifying and smoothing outliers, resolving inconsistencies, schema integration, attribute construction, data cube aggregation, dimensionality reduction, and discretization.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
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 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.
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 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 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 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 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.
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.
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 is crucial for data mining and involves transforming raw data into a clean, consistent format. It includes data cleaning to handle incomplete, noisy, and inconsistent data; data integration of multiple sources; and data transformation through normalization, aggregation, and reduction. The major tasks in data preprocessing are data cleaning, integration, transformation, reduction, and discretization of attributes.
The document discusses data cleaning which aims to fill in missing values, smooth noisy data, identify outliers, and resolve inconsistencies in data. It describes various techniques for handling dirty data issues like missing values, noisy data, and inconsistencies. The goal of data cleaning is to produce high quality data that can be effectively mined to generate meaningful and useful results.
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.
This document discusses module 3 on data pre-processing. It begins with an overview of data quality issues like accuracy, completeness, consistency and timeliness. The major tasks in data pre-processing are then summarized as data cleaning, integration, reduction, and transformation. Data cleaning techniques like handling missing values, noisy data and outliers are covered. The need for feature engineering and dimensionality reduction due to the curse of dimensionality is also highlighted. Finally, techniques for data integration, reduction and discretization are briefly introduced.
Data pre-processing is an important step that includes cleaning, normalization, transformation, feature extraction and selection to produce the final training set. It addresses real-world data issues like incompleteness, noise, and inconsistencies. The main tasks are data cleaning, integration, transformation, reduction, and discretization. Data cleaning fills in missing values and identifies/removes outliers. Data is normalized, aggregated, and generalized. Reduction decreases attributes and tuples through binning, clustering, sampling and other techniques. Data mining tools include traditional programs, dashboards to monitor business performance, and text mining tools to extract structured and unstructured data.
This document discusses various techniques for data processing including data cleaning, integration, transformation, reduction, and similarity/dissimilarity measures. It describes common types of dirty or incomplete data like missing values, noisy data, and inconsistent data. It also discusses techniques for handling different types of dirty data, such as filling in missing values, smoothing noisy data, and resolving inconsistencies. Major tasks in data processing include data cleaning, integration, transformation, and reduction. Specific techniques discussed include binning, clustering, regression, normalization, aggregation, and dimensionality reduction. The document also provides details on various similarity and dissimilarity measures that can be used to calculate the proximity between data objects.
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.
This document discusses data preprocessing techniques. It explains that data is often incomplete, noisy, or inconsistent when collected from the real world. Common preprocessing steps include data cleaning to handle these issues, data integration and transformation to combine multiple data sources, and data reduction to reduce the volume of data for analysis while maintaining analytical results. Specific techniques covered include filling in missing values, identifying and smoothing outliers, resolving inconsistencies, schema integration, attribute construction, data cube aggregation, dimensionality reduction, and discretization.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Preprocessing.ppt
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. Agenda
• Why data preprocessing?
• 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
• A multi-dimensional measure of data quality:
– A well-accepted multi-dimensional view:
• accuracy, completeness, consistency, timeliness, believability, value
added, interpretability, accessibility
– Broad categories:
• intrinsic, contextual, representational, and accessibility.
4. Major Tasks in Data Preprocessing
• Data cleaning
– Fill in missing values, smooth noisy data, identify or remove outliers, and
resolve inconsistencies
• Data integration
– Integration of multiple databases, data cubes, files, or notes
• Data transformation
– Normalization (scaling to a specific range)
– Aggregation
• Data reduction
– Obtains reduced representation in volume but produces the same or similar
analytical results
– Data discretization: with particular importance, especially for numerical data
– Data aggregation, dimensionality reduction, data compression,generalization
6. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
7. Data Cleaning
• Data cleaning tasks
– Fill in missing values
– Identify outliers and smooth out noisy data
– Correct inconsistent data
8. Missing Data
• Data is not always available
– E.g., many tuples have no recorded value for several attributes, such
as customer income in sales data
• Missing data may be due to
– equipment malfunction
– inconsistent with other recorded data and thus deleted
– data not entered due to misunderstanding
– certain data may not be considered important at the time of entry
– not register history or changes of the data
• Missing data may need to be inferred
9. How to Handle Missing Data?
• Ignore the tuple: usually done when class label is missing (assuming the
task is classification—not effective in certain cases)
• 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 of 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 regression, Bayesian formula, decision tree
10. Noisy Data
• Q: What is noise?
• A: Random error in a measured variable.
• Incorrect attribute values may be due to
– faulty data collection instruments
– data entry problems
– data transmission problems
– technology limitation
– inconsistency in naming convention
• Other data problems which requires data cleaning
– duplicate records
– incomplete data
– inconsistent data
11. How to Handle Noisy Data?
• 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.
– used also for discretization (discussed later)
• Clustering
– detect and remove outliers
• Semi-automated method: combined computer and
human inspection
– detect suspicious values and check manually
• Regression
– smooth by fitting the data into regression functions
12. Simple Discretization Methods: Binning
• Equal-width (distance) partitioning:
– It divides the range into N intervals of equal size: uniform
grid
– if A and B are the lowest and highest values of the attribute,
the width of intervals will be: W = (B-A)/N.
– The most straightforward
– But outliers may dominate presentation
– Skewed data is not handled well.
• Equal-depth (frequency) partitioning:
– It divides the range into N intervals, each containing
approximately same number of samples
– Good data scaling
– Managing categorical attributes can be tricky.
13. Binning Methods for Data Smoothing
* Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25,
26, 28, 29, 34
* Partition into (equi-depth) bins:
- Bin 1: 4, 8, 9, 15
- Bin 2: 21, 21, 24, 25
- Bin 3: 26, 28, 29, 34
* Smoothing by bin means:
- Bin 1: 9, 9, 9, 9
- Bin 2: 23, 23, 23, 23
- Bin 3: 29, 29, 29, 29
* Smoothing by bin boundaries:
- Bin 1: 4, 4, 4, 15
- Bin 2: 21, 21, 25, 25
- Bin 3: 26, 26, 26, 34
15. Regression
x
y
y = x + 1
X1
Y1
Y1’
•Linear regression (best line to fit
two variables)
•Multiple linear regression (more
than two variables, fit to a
multidimensional surface
16. How to Handle Inconsistent Data?
• Manual correction using external references
• Semi-automatic using various tools
– To detect violation of known functional
dependencies and data constraints
– To correct redundant data
17. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
18. 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
19. 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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20. 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
21. 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)
22. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
23. 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…
24. •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
25. 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
26. 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
27. 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
28. 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
30. 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
31. • 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
32. 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.
33. 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
34. 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
35. • 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
36. 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
37. 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)
38. 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.
41. 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
42. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
43. 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
44. 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).
45. 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
46. 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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47. 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
49. 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
50. 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
51. Agenda
• Why preprocess the data?
• Data cleaning
• Data integration and transformation
• Data reduction
• Discretization and concept hierarchy generation
• Summary
52. 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