Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques like binning, clustering, regression and histograms are used to discretize and reduce numerical attributes.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
This document discusses classification and prediction. Classification predicts categorical class labels by classifying data based on a training set and class labels. Prediction models continuous values and predicts unknown values. Some applications are credit approval, marketing, medical diagnosis, and treatment analysis. Classification involves a learning step to describe classes and a classification step to classify new data. Prediction involves estimating accuracy by comparing test results to known labels. Issues with classification and prediction include data preparation, comparing methods, and decision tree induction algorithms.
This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
This document discusses classification and prediction. Classification predicts categorical class labels by classifying data based on a training set and class labels. Prediction models continuous values and predicts unknown values. Some applications are credit approval, marketing, medical diagnosis, and treatment analysis. Classification involves a learning step to describe classes and a classification step to classify new data. Prediction involves estimating accuracy by comparing test results to known labels. Issues with classification and prediction include data preparation, comparing methods, and decision tree induction algorithms.
This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
The FP-Growth algorithm allows for the efficient discovery of frequent itemsets without candidate generation. It uses a two-step approach: 1) building a compact FP-tree from transaction data, and 2) extracting frequent itemsets directly from the FP-tree. It proceeds by finding prefix path sub-trees in the FP-tree and recursively mining conditional frequent patterns.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting and model evaluation, discussing metrics, methods of evaluation like cross validation, and comparing models.
This presentation introduces naive Bayesian classification. It begins with an overview of Bayes' theorem and defines a naive Bayes classifier as one that assumes conditional independence between predictor variables given the class. The document provides examples of text classification using naive Bayes and discusses its advantages of simplicity and accuracy, as well as its limitation of assuming independence. It concludes that naive Bayes is a commonly used and effective classification technique.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document discusses feature selection concepts and methods. It defines features as attributes that determine which class an instance belongs to. Feature selection aims to select a relevant subset of features by removing irrelevant, redundant and unnecessary data. This improves learning accuracy, model performance and interpretability. The document categorizes feature selection algorithms as filter, wrapper or embedded methods based on how they evaluate feature subsets. It also discusses concepts like feature relevance, search strategies, successor generation and evaluation measures used in feature selection algorithms.
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
Instance-based learning stores all training instances and classifies new instances based on their similarity to stored examples as determined by a distance metric, typically Euclidean distance. It is a non-parametric approach where the hypothesis complexity grows with the amount of data. K-nearest neighbors specifically finds the K most similar training examples to a new instance and assigns the most common class among those K neighbors. Key aspects are choosing the value of K and the distance metric to evaluate similarity between instances.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
This document provides an overview of classification techniques. It defines classification as assigning records to predefined classes based on their attribute values. The key steps are building a classification model from training data and then using the model to classify new, unseen records. Decision trees are discussed as a popular classification method that uses a tree structure with internal nodes for attributes and leaf nodes for classes. The document covers decision tree induction, handling overfitting, and performance evaluation methods like holdout validation and cross-validation.
A confusion matrix is a table that shows the performance of a classification model by listing the true positives, true negatives, false positives, and false negatives. It displays how often the model correctly or incorrectly classified observations into their actual classes. The document provides an example confusion matrix for a model classifying apples, oranges, and pears, showing the number of observations the model correctly and incorrectly classified into each class.
The document provides an overview of decision tree learning algorithms:
- Decision trees are a supervised learning method that can represent discrete functions and efficiently process large datasets.
- Basic algorithms like ID3 use a top-down greedy search to build decision trees by selecting attributes that best split the training data at each node.
- The quality of a split is typically measured by metrics like information gain, with the goal of creating pure, homogeneous child nodes.
- Fully grown trees may overfit, so algorithms incorporate a bias toward smaller, simpler trees with informative splits near the root.
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
2.6 support vector machines and associative classifiers revisedKrish_ver2
Support vector machines (SVMs) are a type of supervised machine learning model that can be used for both classification and regression analysis. SVMs work by finding a hyperplane in a multidimensional space that best separates clusters of data points. Nonlinear kernels can be used to transform input data into a higher dimensional space to allow for the detection of complex patterns. Associative classification is an alternative approach that uses association rule mining to generate rules describing attribute relationships that can then be used for classification.
CS 402 DATAMINING AND WAREHOUSING -PROBLEMSNIMMYRAJU
This document provides information on various data mining and warehousing techniques including:
1. Normalization methods and binning for data preprocessing
2. Computing Manhattan distance between objects
3. Using the Apriori and FP growth algorithms to find frequent itemsets and generate association rules
Bayesian networks are graphical models that represent conditional independence relationships between variables. A Bayesian network consists of nodes representing variables, and directed edges representing conditional dependencies. It encodes a joint probability distribution over all the variables. Bayesian networks allow efficient inference and can represent incomplete data. They are useful for modeling causal relationships and combining domain knowledge with data.
This document describes the 5 steps of principal component analysis (PCA):
1) Subtract the mean from each dimension of the data to center it around zero.
2) Calculate the covariance matrix of the data.
3) Calculate the eigenvalues and eigenvectors of the covariance matrix.
4) Form a feature vector by selecting eigenvectors corresponding to largest eigenvalues. Project the data onto this to reduce dimensions.
5) To reconstruct the data, take the transpose of the feature vector and multiply it with the projected data, then add the mean back.
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.
The FP-Growth algorithm allows for the efficient discovery of frequent itemsets without candidate generation. It uses a two-step approach: 1) building a compact FP-tree from transaction data, and 2) extracting frequent itemsets directly from the FP-tree. It proceeds by finding prefix path sub-trees in the FP-tree and recursively mining conditional frequent patterns.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting and model evaluation, discussing metrics, methods of evaluation like cross validation, and comparing models.
This presentation introduces naive Bayesian classification. It begins with an overview of Bayes' theorem and defines a naive Bayes classifier as one that assumes conditional independence between predictor variables given the class. The document provides examples of text classification using naive Bayes and discusses its advantages of simplicity and accuracy, as well as its limitation of assuming independence. It concludes that naive Bayes is a commonly used and effective classification technique.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document discusses feature selection concepts and methods. It defines features as attributes that determine which class an instance belongs to. Feature selection aims to select a relevant subset of features by removing irrelevant, redundant and unnecessary data. This improves learning accuracy, model performance and interpretability. The document categorizes feature selection algorithms as filter, wrapper or embedded methods based on how they evaluate feature subsets. It also discusses concepts like feature relevance, search strategies, successor generation and evaluation measures used in feature selection algorithms.
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
Instance-based learning stores all training instances and classifies new instances based on their similarity to stored examples as determined by a distance metric, typically Euclidean distance. It is a non-parametric approach where the hypothesis complexity grows with the amount of data. K-nearest neighbors specifically finds the K most similar training examples to a new instance and assigns the most common class among those K neighbors. Key aspects are choosing the value of K and the distance metric to evaluate similarity between instances.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
This document provides an overview of classification techniques. It defines classification as assigning records to predefined classes based on their attribute values. The key steps are building a classification model from training data and then using the model to classify new, unseen records. Decision trees are discussed as a popular classification method that uses a tree structure with internal nodes for attributes and leaf nodes for classes. The document covers decision tree induction, handling overfitting, and performance evaluation methods like holdout validation and cross-validation.
A confusion matrix is a table that shows the performance of a classification model by listing the true positives, true negatives, false positives, and false negatives. It displays how often the model correctly or incorrectly classified observations into their actual classes. The document provides an example confusion matrix for a model classifying apples, oranges, and pears, showing the number of observations the model correctly and incorrectly classified into each class.
The document provides an overview of decision tree learning algorithms:
- Decision trees are a supervised learning method that can represent discrete functions and efficiently process large datasets.
- Basic algorithms like ID3 use a top-down greedy search to build decision trees by selecting attributes that best split the training data at each node.
- The quality of a split is typically measured by metrics like information gain, with the goal of creating pure, homogeneous child nodes.
- Fully grown trees may overfit, so algorithms incorporate a bias toward smaller, simpler trees with informative splits near the root.
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
2.6 support vector machines and associative classifiers revisedKrish_ver2
Support vector machines (SVMs) are a type of supervised machine learning model that can be used for both classification and regression analysis. SVMs work by finding a hyperplane in a multidimensional space that best separates clusters of data points. Nonlinear kernels can be used to transform input data into a higher dimensional space to allow for the detection of complex patterns. Associative classification is an alternative approach that uses association rule mining to generate rules describing attribute relationships that can then be used for classification.
CS 402 DATAMINING AND WAREHOUSING -PROBLEMSNIMMYRAJU
This document provides information on various data mining and warehousing techniques including:
1. Normalization methods and binning for data preprocessing
2. Computing Manhattan distance between objects
3. Using the Apriori and FP growth algorithms to find frequent itemsets and generate association rules
Bayesian networks are graphical models that represent conditional independence relationships between variables. A Bayesian network consists of nodes representing variables, and directed edges representing conditional dependencies. It encodes a joint probability distribution over all the variables. Bayesian networks allow efficient inference and can represent incomplete data. They are useful for modeling causal relationships and combining domain knowledge with data.
This document describes the 5 steps of principal component analysis (PCA):
1) Subtract the mean from each dimension of the data to center it around zero.
2) Calculate the covariance matrix of the data.
3) Calculate the eigenvalues and eigenvectors of the covariance matrix.
4) Form a feature vector by selecting eigenvectors corresponding to largest eigenvalues. Project the data onto this to reduce dimensions.
5) To reconstruct the data, take the transpose of the feature vector and multiply it with the projected data, then add the mean back.
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 preprocessing is important for data mining and involves data cleaning, integration, reduction, and discretization. The goals are to handle missing data, remove noise, resolve inconsistencies, reduce data size for faster mining, and prepare data for modeling. Common techniques include filling in missing values, smoothing noisy data, aggregating data, normalizing values, selecting important features, clustering data, and discretizing continuous variables. Preprocessing helps produce higher quality mining results from dirtier real-world data.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, handle inconsistencies, and reduce data volume for analysis while retaining essential information. Techniques include discretization, concept hierarchy generation, sampling, clustering, and developing histograms to obtain a reduced data representation.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and convert continuous attributes to discrete intervals or concepts. Preprocessing helps produce higher quality mining results.
Data preprocessing is an important step for data mining and warehousing. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, and reducing data. The goals are to improve data quality, reduce data size, and prepare data for mining algorithms. Key techniques include data cleaning, discretization of continuous attributes, feature selection, and various data reduction methods like binning, clustering, and sampling. Preprocessing helps produce higher quality mining results from quality data.
Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes data integration and transformation through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality, handle inconsistencies, and reduce data size for mining. Techniques include binning, clustering, sampling and discretization which create intervals or concept hierarchies to generalize continuous attributes for analysis.
Data preprocessing is an important step for data mining and warehousing. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, and reducing data. The goals are to improve data quality, reduce data size, and prepare data for mining algorithms. Key techniques include data cleaning, discretization of continuous attributes, feature selection, and various data reduction methods like binning, clustering, and sampling. Preprocessing helps produce higher quality mining results based on higher quality input data.
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying and handling outliers, aggregating data, feature selection, normalization, binning, clustering, and generating concept hierarchies. Preprocessing addresses issues like dirty, incomplete, inconsistent or redundant data to improve mining quality and efficiency.
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying outliers, aggregating data, feature selection, binning, clustering, and generating concept hierarchies to replace raw values with semantic concepts. Preprocessing addresses issues like dirty, incomplete, inconsistent data to produce high quality input for mining models and decisions.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
Data Preprocessing can be defined as a process of converting raw data into a format that is understandable and usable for further analysis. It is an important step in the Data Preparation stage. It ensures that the outcome of the analysis is accurate, complete, and consistent.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, reduce data size for analysis, and prepare data for mining algorithms through techniques like discretization and concept hierarchy generation.
Data preprocessing involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating and transforming data by normalization, aggregation, and reduction. The document discusses techniques for data cleaning like binning and clustering to handle noisy data. It also covers data integration, transformation through normalization, and reduction using histograms, clustering, and sampling. Discretization and concept hierarchies are introduced as techniques to reduce continuous attributes for data analysis.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
This document discusses data preparation techniques for data warehousing and mining projects, including descriptive data summarization, data cleaning, integration and transformation, and reduction. It covers cleaning techniques like handling missing data, identifying outliers, and resolving inconsistencies. Data integration challenges like schema matching and resolving conflicts are also addressed. Methods for data reduction like aggregation, generalization, normalization and attribute construction are summarized.
Data preprocessing involves several key steps:
1) Data cleaning to fill in missing values, identify and remove outliers, and resolve inconsistencies
2) Data integration to combine multiple data sources and resolve conflicts and redundancies
3) Data reduction techniques like discretization, dimensionality reduction, and aggregation to obtain a reduced representation of the data for mining and analysis.
The document discusses various techniques for data preparation and preprocessing for data warehousing and mining projects. It covers descriptive data summarization, data cleaning, integration and transformation, and reduction. The key aspects covered include handling missing data, resolving inconsistencies, reducing redundancy through integration, and reducing data volume through techniques like aggregation, generalization and discretization while maintaining analytical capabilities. Quality data preparation is emphasized as essential for obtaining quality mining results.
This document discusses three types of hardware multithreading: coarse-grained, fine-grained, and simultaneous multithreading (SMT). Coarse-grained multithreading allows another thread to run during long stalls of the first thread. Fine-grained multithreading interleaves instructions from multiple threads in a round-robin fashion to hide stalls. SMT issues instructions from multiple threads in the same cycle by using register renaming and dynamic scheduling to maximize utilization.
The document discusses the Lisp programming language. It notes that Allegro Common Lisp will be used and lists textbooks for learning Lisp. It provides 10 points on Lisp, including that it is interactive, dynamic, uses symbols and lists as basic data types, prefix notation for operators, and classifies different data types. Evaluation follows simple rules and programs can be treated as both instructions and data.
Simultaneous multithreading (SMT) allows multiple independent threads to issue and execute instructions simultaneously each clock cycle by sharing the functional units of a superscalar processor. This improves performance over conventional multithreading approaches like coarse-grained and fine-grained multithreading. SMT provides good performance across a wide range of workloads by utilizing instruction issue slots and execution resources that would otherwise go unused when a single thread is limited by dependencies or cache misses. Implementing SMT requires minimal additional hardware like multiple program counters and per-thread scheduling structures.
The document discusses non-uniform cache architectures (NUCA), cache coherence, and different implementations of directories in multicore systems. It describes NUCA designs that map data to banks based on distance from the controller to exploit non-uniform access times. Cache coherence is maintained using directory-based protocols that track copies of cache blocks. Directories can be implemented off-chip in DRAM or on-chip using duplicate tag stores or distributing the directory among cache banks.
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on the whole business or segments. Types of BA include reporting/descriptive analytics using tools like affinity grouping and clustering, as well as predictive analytics using modeling. Challenges include acquiring high quality data and reacting to data quickly. Data mining is important for BA as it helps handle large datasets and specific problems in conducting analytics.
This document discusses decision trees and how they are constructed. It begins by explaining that decision trees use supervised learning to generate classification rules by splitting a training dataset based on attribute values. It then walks through an example of constructing a decision tree for predicting voter support based on attributes like age, income, education level, etc. The document discusses that decision trees are constructed recursively by choosing the attribute that creates the "purest" splits at each node, often using an information gain heuristic that favors splits lowering entropy.
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large datasets. Data mining involves automated analysis techniques from fields like machine learning, statistics, and database management to discover patterns and relationships in data. The knowledge discovery process involves data preparation, data mining, and evaluation of the extracted patterns. The document provides examples of data mining applications in business, science, fraud detection, and web mining.
The document discusses memory hierarchy and caching techniques. It begins by explaining the need for a memory hierarchy due to differing access times of memory technologies like SRAM, DRAM, and disk. It then covers concepts like cache hits, misses, block size, direct mapping, set associativity, compulsory misses, capacity misses, and conflict misses. It also discusses techniques for improving cache performance like multi-level caches, write buffers, increasing associativity, and interleaving memory banks.
This document discusses how Analysis Services caching works and provides strategies for warming the Storage Engine cache and Formula Engine cache. It explains that the Storage Engine handles data retrieval from disk while the Formula Engine determines which data is needed for queries. Caching can improve performance but requires understanding when Analysis Services is unable to cache data. The document recommends using the CREATE CACHE statement and running regular queries to pre-populate the caches with commonly used data. Memory usage must be monitored when warming caches to avoid exceeding limits. Automating cache warming after processing is suggested to not interfere with user queries.
The document proposes optimizing DRAM caches for latency rather than hit rate. It summarizes previous work on DRAM caches like Loh-Hill Cache that treated DRAM cache similarly to SRAM cache. This led to high latency and low bandwidth utilization.
The document introduces the Alloy Cache design which avoids tag serialization and keeps tags and data in the same DRAM row for lower latency. It also proposes a simple Memory Access Predictor to use either serial or parallel access models depending on the prediction to reduce latency and bandwidth usage. Simulation results show the Alloy Cache with predictor outperforms previous designs like SRAM-Tags.
The document discusses abstract data types (ADTs), specifically queues. It defines a queue as a linear collection where elements are added to one end and removed from the other end, following a first-in, first-out (FIFO) approach. The key queue operations are enqueue, which adds an element, and dequeue, which removes the element that has been in the queue longest. Queues can be implemented using arrays or linked lists. Array implementations use head and tail pointers to track the start and end of the queue.
The document provides information on three programming languages: COBOL, LISP, and Python. COBOL was released in 1959 and was used for 80% of business transactions due to its reliability. LISP was the second high-level language created in 1958 and introduced innovations like garbage collection and recursion using linked lists. Python was developed in the 1990s and prioritizes readability through features like whitespace and a simple grammar.
This document discusses abstract data types (ADTs) and their implementation in various programming languages. It covers the key concepts of ADTs including data abstraction, encapsulation, information hiding, and defining the public interface separately from the private implementation. It provides examples of ADTs implemented using modules in Modula-2, packages in Ada, classes in C++, generics in Java and C#, and classes in Ruby. Parameterized and encapsulation constructs are also discussed as techniques for implementing and organizing ADTs.
Optimizing shared caches in chip multiprocessorsFraboni Ec
Chip multiprocessors, which place multiple processors on a single chip, have become common in modern processors. There are different approaches to managing caches in chip multiprocessors, including private caches for each processor or shared caches. The optimal approach balances factors like interconnect traffic, duplication of data, load balancing, and cache hit rates.
This document discusses the key concepts of object-oriented programming including abstraction, encapsulation, classes and objects. It defines abstraction as focusing on the essential characteristics of an object and hiding unnecessary details. Encapsulation hides the internal representation of an object within its class. A class defines both the data and behaviors of an object through its public interface and private implementation. Objects are instantiations of classes that come to life through constructors and die through destructors while maintaining data integrity.
The document discusses abstraction, which is a fundamental concept of object-oriented design. Abstraction involves focusing on an object's essential characteristics and behavior while hiding implementation details. There are different types of abstractions from most useful to least useful. Effective abstractions model real-world entities and provide well-defined interfaces through contracts, preconditions, and postconditions. Both static and dynamic properties of objects must be considered.
Object-oriented analysis and design (OOAD) emphasizes investigating requirements rather than solutions, and conceptual solutions that fulfill requirements rather than implementations. OOAD focuses on identifying domain concepts and defining software objects and how they collaborate. The document then discusses OO concepts like encapsulation, abstraction, inheritance, and polymorphism and how classes and objects are used in object-oriented programming. It provides an overview of the course structure and evaluation criteria.
Abstract classes and interfaces allow for abstraction and polymorphism in object-oriented design. Abstract classes can contain both abstract and concrete methods, while interfaces only contain abstract methods. Abstract classes are used to provide a common definition for subclasses through inheritance, while interfaces define a contract for implementing classes to follow. Both increase complexity, so their use should provide clear benefits to functionality.
This document discusses various programming paradigms and concurrency concepts in Java. It covers single process and multi-process programming, as well as multi-core and multi-threaded programming. Key concepts discussed include processes, threads, synchronization, deadlocks, and high-level concurrency objects like locks, executors, and concurrent collections. The document provides examples of implementing and managing threads, as well as communicating between threads using techniques like interrupts, joins, and guarded blocks.
This document discusses inheritance in object-oriented programming. It explains that inheritance allows a subclass to inherit attributes and behaviors from a superclass, extending the superclass. This allows for code reuse and the establishment of class hierarchies. The document provides an example of a BankAccount superclass and SavingsAccount subclass, demonstrating how the subclass inherits methods like deposit() and withdraw() from the superclass while adding its own method, addInterest(). It also discusses polymorphism and access control as related concepts.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
2. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
3. Why Data Preprocessing?
Data in the real world is dirty
incomplete: lacking attribute values, lacking certain
attributes of interest, or containing only aggregate data
noisy: containing errors or outliers
inconsistent: containing discrepancies in codes or
names
No quality data, no quality mining results!
Quality decisions must be based on quality data
Data warehouse needs consistent integration of quality
data
Required for both OLAP and Data Mining!
4. Why can Data be Incomplete?
Attributes of interest are not available (e.g.,
customer information for sales transaction data)
Data were not considered important at the time
of transactions, so they were not recorded!
Data not recorder because of misunderstanding
or malfunctions
Data may have been recorded and later deleted!
Missing/unknown values for some data
5. Why can Data be Noisy/Inconsistent?
Faulty instruments for data collection
Human or computer errors
Errors in data transmission
Technology limitations (e.g., sensor data come at
a faster rate than they can be processed)
Inconsistencies in naming conventions or data
codes (e.g., 2/5/2002 could be 2 May 2002 or 5
Feb 2002)
Duplicate tuples, which were received twice
should also be removed
6. Major Tasks in Data Preprocessing
Data cleaning
Fill in missing values, smooth noisy data, identify or remove
outliers, and resolve inconsistencies
Data integration
Integration of multiple databases, data cubes, or files
Data transformation
Normalization and aggregation
Data reduction
Obtains reduced representation in volume but produces the
same or similar analytical results
Data discretization
Part of data reduction but with particular importance,
especially for numerical data
outliers=exceptions!
8. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
9. Data Cleaning
Data cleaning tasks
Fill in missing values
Identify outliers and smooth out noisy data
Correct inconsistent data
10. How to Handle Missing Data?
Ignore the tuple: usually done when class label is missing
(assuming the tasks in classification)—not effective when the
percentage of missing values per attribute varies considerably.
Fill in the missing value manually: tedious + infeasible?
Use a global constant to fill in the missing value: e.g.,
“unknown”, a new class?!
Use the attribute mean to fill in the missing value
Use the attribute mean for all samples belonging to the same
class to fill in the missing value: smarter
Use the most probable value to fill in the missing value:
inference-based such as Bayesian formula or decision tree
11. How to Handle Missing Data?
Age Income Religion Gender
23 24,200 Muslim M
39 ? Christian F
45 45,390 ? F
Fill missing values using aggregate functions (e.g., average) or
probabilistic estimates on global value distribution
E.g., put the average income here, or put the most probable income
based on the fact that the person is 39 years old
E.g., put the most frequent religion here
12. Noisy Data
Noise: random error or variance in a measured
variable
Incorrect attribute values may exist due to
faulty data collection instruments
data entry problems
data transmission problems
technology limitation
inconsistency in naming convention
Other data problems which requires data cleaning
duplicate records
incomplete data
inconsistent data
13. How to Handle Noisy Data?
Smoothing techniques
Binning method:
first sort data and partition into (equi-depth) bins
then one can smooth by bin means, smooth by bin
median, smooth by bin boundaries, etc.
Clustering
detect and remove outliers
Combined computer and human inspection
computer detects suspicious values, which are then
checked by humans
Regression
smooth by fitting the data into regression functions
Use Concept hierarchies
use concept hierarchies, e.g., price value -> “expensive”
14. Simple Discretization Methods: Binning
Equal-width (distance) partitioning:
It divides the range into N intervals of equal size:
uniform grid
if A and B are the lowest and highest values of the
attribute, the width of intervals will be: W = (B-A)/N.
The most straightforward
But outliers may dominate presentation
Skewed data is not handled well.
Equal-depth (frequency) partitioning:
It divides the range into N intervals, each containing
approximately same number of samples
Good data scaling – good handing of skewed data
19. Inconsistent Data
Inconsistent data are handled by:
Manual correction (expensive and tedious)
Use routines designed to detect inconsistencies
and manually correct them. E.g., the routine may
use the check global constraints (age>10) or
functional dependencies
Other inconsistencies (e.g., between names of
the same attribute) can be corrected during the
data integration process
20. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
21. Data Integration
Data integration:
combines data from multiple sources into a coherent store
Schema integration
integrate metadata from different sources
metadata: data about the data (i.e., data descriptors)
Entity identification problem: identify real world entities
from multiple data sources, e.g., A.cust-id ≡ B.cust-#
Detecting and resolving data value conflicts
for the same real world entity, attribute values from
different sources are different (e.g., J.D.Smith and Jonh
Smith may refer to the same person)
possible reasons: different representations, different
scales, e.g., metric vs. British units (inches vs. cm)
22. Handling Redundant
Data in Data Integration
Redundant data occur often when integration of
multiple databases
The same attribute may have different names in different
databases
One attribute may be a “derived” attribute in another
table, e.g., annual revenue
Redundant data may be able to be detected by
correlation analysis
Careful integration of the data from multiple
sources may help reduce/avoid redundancies and
inconsistencies and improve mining speed and
quality
23. Data Transformation
Smoothing: remove noise from data
Aggregation: summarization, data cube
construction
Generalization: concept hierarchy climbing
Normalization: scaled to fall within a small,
specified range
min-max normalization
z-score normalization
normalization by decimal scaling
Attribute/feature construction
New attributes constructed from the given ones
24. Normalization: Why normalization?
Speeds-up some learning techniques (ex.
neural networks)
Helps prevent attributes with large ranges
outweigh ones with small ranges
Example:
income has range 3000-200000
age has range 10-80
gender has domain M/F
25. Data Transformation: Normalization
min-max normalization
e.g. convert age=30 to range 0-1, when
min=10,max=80. new_age=(30-10)/(80-10)=2/7
z-score normalization
normalization by decimal scaling
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26. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
27. Data Reduction Strategies
Warehouse may store terabytes of data: Complex
data analysis/mining may take a very long time to
run on the complete data set
Data reduction
Obtains a reduced representation of the data set that is
much smaller in volume but yet produces the same (or
almost the same) analytical results
Data reduction strategies
Data cube aggregation
Dimensionality reduction
Data compression
Numerosity reduction
Discretization and concept hierarchy generation
28. Data Cube Aggregation
The lowest level of a data cube
the aggregated data for an individual entity of interest
e.g., a customer in a phone calling data warehouse.
Multiple levels of aggregation in data cubes
Further reduce the size of data to deal with
Reference appropriate levels
Use the smallest representation which is enough to solve
the task
Queries regarding aggregated information should be
answered using data cube, when possible
29. Dimensionality Reduction
Feature selection (i.e., attribute subset selection):
Select a minimum set of features such that the probability
distribution of different classes given the values for those
features is as close as possible to the original distribution
given the values of all features
reduce # of patterns in the patterns, easier to understand
Heuristic methods (due to exponential # of
choices):
step-wise forward selection
step-wise backward elimination
combining forward selection and backward elimination
decision-tree induction
30. Heuristic Feature Selection Methods
There are 2d
possible sub-features of d features
Several heuristic feature selection methods:
Best single features under the feature independence
assumption: choose by significance tests.
Best step-wise feature selection:
The best single-feature is picked first
Then next best feature condition to the first, ...
Step-wise feature elimination:
Repeatedly eliminate the worst feature
Best combined feature selection and elimination:
Optimal branch and bound:
Use feature elimination and backtracking
31. Example of Decision Tree Induction
Initial attribute set:
{A1, A2, A3, A4, A5, A6}
A4 ?
A1? A6?
Class 1 Class 2 Class 1 Class 2
> Reduced attribute set: {A1, A4, A6}
33. Given N data vectors from k-dimensions, find c
<= k orthogonal vectors that can be best used
to represent data
The original data set is reduced to one consisting of N
data vectors on c principal components (reduced
dimensions)
Each data vector is a linear combination of the c
principal component vectors
Works for numeric data only
Used when the number of dimensions is large
Principal Component Analysis or
Karhuren-Loeve (K-L) method
34. X1
X2
Y1
Y2
Principal Component Analysis
X1, X2: original axes (attributes)
Y1,Y2: principal components
significant component
(high variance)
Order principal components by significance and eliminate weaker ones
35. Numerosity Reduction:
Reduce the volume of data
Parametric methods
Assume the data fits some model, estimate model
parameters, store only the parameters, and discard the
data (except possible outliers)
Log-linear models: obtain value at a point in m-D
space as the product on appropriate marginal
subspaces
Non-parametric methods
Do not assume models
Major families: histograms, clustering, sampling
36. Histograms
A popular data
reduction technique
Divide data into
buckets and store
average (or sum) for
each bucket
Can be constructed
optimally in one
dimension using
dynamic
programming
Related to
quantization
problems.
0
5
10
15
20
25
30
35
40
10000 30000 50000 70000 90000
37. Histogram types
Equal-width histograms:
It divides the range into N intervals of equal size
Equal-depth (frequency) partitioning:
It divides the range into N intervals, each containing
approximately same number of samples
V-optimal:
It considers all histogram types for a given number of
buckets and chooses the one with the least variance.
MaxDiff:
After sorting the data to be approximated, it defines the
borders of the buckets at points where the adjacent
values have the maximum difference
Example: split 1,1,4,5,5,7,9,14,16,18,27,30,30,32 to three
buckets
MaxDiff 27-18 and 14-9
Histograms
38. Clustering
Partitions data set into clusters, and models it by
one representative from each cluster
Can be very effective if data is clustered but not
if data is “smeared”
There are many choices of clustering definitions
and clustering algorithms, further detailed in
Chapter 7
40. Hierarchical Reduction
Use multi-resolution structure with different
degrees of reduction
Hierarchical clustering is often performed but tends
to define partitions of data sets rather than
“clusters”
Parametric methods are usually not amenable to
hierarchical representation
Hierarchical aggregation
An index tree hierarchically divides a data set into
partitions by value range of some attributes
Each partition can be considered as a bucket
Thus an index tree with aggregates stored at each node is
a hierarchical histogram
41. Data Preprocessing
Why preprocess the data?
Data cleaning
Data integration and transformation
Data reduction
Discretization and concept hierarchy
generation
Summary
42. Discretization
Three types of attributes:
Nominal — values from an unordered set
Ordinal — values from an ordered set
Continuous — real numbers
Discretization:
divide the range of a continuous attribute into
intervals
why?
Some classification algorithms only accept
categorical attributes.
Reduce data size by discretization
Prepare for further analysis
43. Discretization and Concept hierachy
Discretization
reduce the number of values for a given continuous
attribute by dividing the range of the attribute into
intervals. Interval labels can then be used to replace
actual data values.
Concept hierarchies
reduce the data by collecting and replacing low level
concepts (such as numeric values for the attribute age)
by higher level concepts (such as young, middle-aged,
or senior).
44. Discretization and concept hierarchy
generation for numeric data
Binning/Smoothing (see sections before)
Histogram analysis (see sections before)
Clustering analysis (see sections before)
Entropy-based discretization
Segmentation by natural partitioning
45. Entropy-Based Discretization
Given a set of samples S, if S is partitioned into
two intervals S1 and S2 using boundary T, the
information gain I(S,T) after partitioning is
The boundary that maximizes the information gain
over all possible boundaries is selected as a binary
discretization.
The process is recursively applied to partitions
obtained until some stopping criterion is met, e.g.,
Experiments show that it may reduce data size
and improve classification accuracy
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46. Segmentation by natural partitioning
The 3-4-5 rule can be used to segment numerical data into
relatively uniform, “natural” intervals.
* If an interval covers 3, 6, 7 or 9 distinct values at the most
significant digit, partition the range into 3 equiwidth intervals
for 3,6,9 or 2-3-2 for 7
* If it covers 2, 4, or 8 distinct values at the most significant
digit, partition the range into 4 equiwidth intervals
* If it covers 1, 5, or 10 distinct values at the most significant
digit, partition the range into 5 equiwidth intervals
Users often like to see numerical ranges partitioned into
relatively uniform, easy-to-read intervals that appear intuitive
or “natural”. E.g., [50-60] better than [51.223-60.812]
The rule can be recursively applied for the resulting intervals
47. Concept hierarchy generation for
categorical data
Categorical attributes: finite, possibly large domain, with no
ordering among the values
Example: item type
Specification of a partial ordering of attributes explicitly at
the schema level by users or experts
Example: location is split by domain experts to
street<city<state<country
Specification of a portion of a hierarchy by explicit data
grouping
Specification of a set of attributes, but not of their partial
ordering
Specification of only a partial set of attributes
48. Specification of a set of attributes
Concept hierarchy can be automatically
generated based on the number of distinct
values per attribute in the given attribute set.
The attribute with the most distinct values is
placed at the lowest level of the hierarchy.
country
province_or_ state
city
street
15 distinct values
65 distinct
values
3567 distinct values
674,339 distinct values