Automating Objective Data Quality Assessment
... much wider than that of information accuracy, we will use those terms interchangeably. Even in the more narrow case of information accuracy, the objective assessment is often limited by multiple practicality factors. In many situations it is much easier to verify correctness of data’s syntactic prop ...
... much wider than that of information accuracy, we will use those terms interchangeably. Even in the more narrow case of information accuracy, the objective assessment is often limited by multiple practicality factors. In many situations it is much easier to verify correctness of data’s syntactic prop ...
Technical report MSU-CSE-04-35
... As a corollary, the above heuristic suggests that the difference between the rankings provided by the true preference function and the rankings provided by each of the individual ordering functions should be minimal. At each iterative step of our unsupervised Hedge algorithm, instead of comparing th ...
... As a corollary, the above heuristic suggests that the difference between the rankings provided by the true preference function and the rankings provided by each of the individual ordering functions should be minimal. At each iterative step of our unsupervised Hedge algorithm, instead of comparing th ...
A statistical perspective on data mining
... values of the class variable cover a continuous range. To illustrate the range of approaches available in statistics and data mining we consider the classi cation problem. Many dierent methods are used for classi cation. The classical statistical approach is discriminant analysis; starting from thi ...
... values of the class variable cover a continuous range. To illustrate the range of approaches available in statistics and data mining we consider the classi cation problem. Many dierent methods are used for classi cation. The classical statistical approach is discriminant analysis; starting from thi ...
A Result Evolution Approach for Web usage mining using Fuzzy C
... according to behavioral rules to allow each ant to find the label (or nest) that best fits its genome. AntClust does not need to be initialized with the expected number of clusters and runs in linear time with the number of objects. AntClust has also been successfully applied to the web sessions clu ...
... according to behavioral rules to allow each ant to find the label (or nest) that best fits its genome. AntClust does not need to be initialized with the expected number of clusters and runs in linear time with the number of objects. AntClust has also been successfully applied to the web sessions clu ...
Data Mining
... A) A set of databases from different vendors, possibly using different database paradigms B) An approach to a problem that is not guaranteed to work but performs well in most cases C) Information that is hidden in a database and that cannot be recovered by a simple SQL query. D) None of these ...
... A) A set of databases from different vendors, possibly using different database paradigms B) An approach to a problem that is not guaranteed to work but performs well in most cases C) Information that is hidden in a database and that cannot be recovered by a simple SQL query. D) None of these ...
Lecture Slides
... Allow easier visualization Dimensionality reduction techniques Principal component analysis Singular value decomposition Supervised and nonlinear techniques (e.g., feature selection) ...
... Allow easier visualization Dimensionality reduction techniques Principal component analysis Singular value decomposition Supervised and nonlinear techniques (e.g., feature selection) ...
Data Mining - Web Access for Home
... Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, patternbased classification, logistic regression, … ...
... Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, patternbased classification, logistic regression, … ...
slides in pdf - Università degli Studi di Milano
... Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers) → already “data mining” Data migration and integration Data migration tools: allow transformations to be specified ...
... Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers) → already “data mining” Data migration and integration Data migration tools: allow transformations to be specified ...
A COMP 790-090 Data Mining
... Gini index (CART, IBM IntelligentMiner) All attributes are assumed continuous-valued Assume there exist several possible split values for each attribute May need other tools, such as clustering, to get the possible split values Can be modified for categorical attributes ...
... Gini index (CART, IBM IntelligentMiner) All attributes are assumed continuous-valued Assume there exist several possible split values for each attribute May need other tools, such as clustering, to get the possible split values Can be modified for categorical attributes ...
A COMP 790-090 Data Mining - UNC Computer Science
... Gini index (CART, IBM IntelligentMiner) All attributes are assumed continuous-valued Assume there exist several possible split values for each attribute May need other tools, such as clustering, to get the possible split values Can be modified for categorical attributes ...
... Gini index (CART, IBM IntelligentMiner) All attributes are assumed continuous-valued Assume there exist several possible split values for each attribute May need other tools, such as clustering, to get the possible split values Can be modified for categorical attributes ...
View PDF - CiteSeerX
... Transformation is converting the data into a common format for processing. Some data may be encoded or transformed into more usable format. Data reduction, dimensionality reduction (e.g. feature selection i.e. attribute subset selection, heuristic method etc) & data transformation method (e.g. sampl ...
... Transformation is converting the data into a common format for processing. Some data may be encoded or transformed into more usable format. Data reduction, dimensionality reduction (e.g. feature selection i.e. attribute subset selection, heuristic method etc) & data transformation method (e.g. sampl ...
slide
... Move to Other Solutions ・ For maximal solutions, - remove some elements and add others to be maximal can move iteratively to any solution - but, #neighboring solutions is exponential, enumeration would take exponential time for each ・ restrict the move to reduce the neighbors - add a key element ...
... Move to Other Solutions ・ For maximal solutions, - remove some elements and add others to be maximal can move iteratively to any solution - but, #neighboring solutions is exponential, enumeration would take exponential time for each ・ restrict the move to reduce the neighbors - add a key element ...
Integrating Data Mining into Feedback Loops for Predictive Context
... For example, it would take hours, or even days for a human to analyze even the relatively small data set used in this study, and derive useful results. It is often infeasible for human analysts to interpret the vast amounts of contextual data produced by mobile and other ubiquitous systems for conte ...
... For example, it would take hours, or even days for a human to analyze even the relatively small data set used in this study, and derive useful results. It is often infeasible for human analysts to interpret the vast amounts of contextual data produced by mobile and other ubiquitous systems for conte ...
Statistics and Machine Learning at Scale
... With reinforcement learning, the algorithm discovers for itself which actions yield the greatest rewards through trial and error. Reinforcement learning has three primary components: 1. The agent – the learner or decision maker. 2. The environment – everything the agent interacts with. 3. Actions ...
... With reinforcement learning, the algorithm discovers for itself which actions yield the greatest rewards through trial and error. Reinforcement learning has three primary components: 1. The agent – the learner or decision maker. 2. The environment – everything the agent interacts with. 3. Actions ...
Introduction to Big Data Analytics
... • “Big data is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value.” • [source: C. Manyika, Big Data: The next frontier for innovation, competition, and productivity, Mc ...
... • “Big data is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value.” • [source: C. Manyika, Big Data: The next frontier for innovation, competition, and productivity, Mc ...
Data Mining
... summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Interpretation and evaluation: analysis of results. ...
... summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Interpretation and evaluation: analysis of results. ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.