Statistics Made Easy
... example) • In a t-test, in order to be statistically significant the t score needs to be in the “blue zone” • If α = .05, then 2.5% of the area is in each tail ...
... example) • In a t-test, in order to be statistically significant the t score needs to be in the “blue zone” • If α = .05, then 2.5% of the area is in each tail ...
Data Analytic
... Han, J., M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Elsevier, Amsterdam. Textbook. Year of Publication 2012 James, G., D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical learning with Application to R, Springer, New York. Year of Publication 2013 Jank, W., Busi ...
... Han, J., M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Elsevier, Amsterdam. Textbook. Year of Publication 2012 James, G., D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical learning with Application to R, Springer, New York. Year of Publication 2013 Jank, W., Busi ...
An introduction to Support Vector Machines
... Subject to: w = iyixi iyi = 0 Another kernel example: The polynomial kernel K(xi,xj) = (xi•xj + 1)p, where p is a tunable parameter. Evaluating K only require one addition and one exponentiation more than the original dot product. ...
... Subject to: w = iyixi iyi = 0 Another kernel example: The polynomial kernel K(xi,xj) = (xi•xj + 1)p, where p is a tunable parameter. Evaluating K only require one addition and one exponentiation more than the original dot product. ...
Data Mining - Evaluation of Classifiers
... • It is important that the test data is not used in any way to create the classifier! • One random split is used for really large data • For medium sized → repeated hold-out • Holdout estimate can be made more reliable by repeating the process with different subsamples • In each iteration, a certain ...
... • It is important that the test data is not used in any way to create the classifier! • One random split is used for really large data • For medium sized → repeated hold-out • Holdout estimate can be made more reliable by repeating the process with different subsamples • In each iteration, a certain ...
Machine Learning
... Data Mining, Inference and Prediction. Springer Series in Statistics. Springer, (2003 a later, web) ...
... Data Mining, Inference and Prediction. Springer Series in Statistics. Springer, (2003 a later, web) ...
Streaming Algorithms for Clustering and Learning
... Data mining and machine learning techniques have become very important for automatic data analysis. Our goal is to design algorithms for performing these computations that are suitable for Massive Data Sets. The streaming model of computation models many of the important constraints imposed by compu ...
... Data mining and machine learning techniques have become very important for automatic data analysis. Our goal is to design algorithms for performing these computations that are suitable for Massive Data Sets. The streaming model of computation models many of the important constraints imposed by compu ...
assignment 3 - Iain Pardoe
... each customer will spend the “average”). Later in the course we will discuss models that will allow us to predict whether a customer will make a purchase if we send them a catalog (again with a reasonable accuracy that is much better than sending out catalogs at random). We can then multiply the pro ...
... each customer will spend the “average”). Later in the course we will discuss models that will allow us to predict whether a customer will make a purchase if we send them a catalog (again with a reasonable accuracy that is much better than sending out catalogs at random). We can then multiply the pro ...
Data Mining & Analysis
... • “The area of computer science which deals with problems, that we where not able to cope with before.” – Computer science is a branch of mathematics, btw. ...
... • “The area of computer science which deals with problems, that we where not able to cope with before.” – Computer science is a branch of mathematics, btw. ...
classification - Computing and Information Studies
... ii. If not, use some attribute selection criterion. Suppose we use information gain. Compute the information gain for each attribute and select that attribute which has the highest information gain. This becomes the root node. iii. Divide the data according to the attribute values of the root node. ...
... ii. If not, use some attribute selection criterion. Suppose we use information gain. Compute the information gain for each attribute and select that attribute which has the highest information gain. This becomes the root node. iii. Divide the data according to the attribute values of the root node. ...
Database Management System
... Map raw data (x1, x2 points) into the two vectors.. Use the vectors instead of the raw data. ...
... Map raw data (x1, x2 points) into the two vectors.. Use the vectors instead of the raw data. ...
KSE525 - Data Mining Lab
... The distance function is the Euclidean distance. center of each cluster, respectively. after the first round of execution. ...
... The distance function is the Euclidean distance. center of each cluster, respectively. after the first round of execution. ...
Referências para as disciplinas de Modelagem
... [3] Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd edition, 2009. [4] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. ...
... [3] Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd edition, 2009. [4] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. ...
General Mining Issues
... expected classification performance of the ML-technique. The available data is again divided in k subsets and again the MLalgorithm is trained k times (in combination with the parameter setting as selected in Step 1). The average classification performance on the k test sets is used to estimate the ...
... expected classification performance of the ML-technique. The available data is again divided in k subsets and again the MLalgorithm is trained k times (in combination with the parameter setting as selected in Step 1). The average classification performance on the k test sets is used to estimate the ...
Questions October 4
... In the news clustering problem we computed the distance between two news entities based on their (key-) wordlists A and B as follows: distance(A,B)=1-(|AB)|/|AB|) with ‘||’ denoting set cardinality; e.g. |{a,b}|=2. Why do we divide by (AB) in the formula? 2. What is the main difference between or ...
... In the news clustering problem we computed the distance between two news entities based on their (key-) wordlists A and B as follows: distance(A,B)=1-(|AB)|/|AB|) with ‘||’ denoting set cardinality; e.g. |{a,b}|=2. Why do we divide by (AB) in the formula? 2. What is the main difference between or ...
classification - Computer and Information Science
... An important premise of data mining is that databases are rich with hidden information which could be useful for decision making. The information we seek can be in many different forms and could be used for a verity of decision making tasks. An important type of decision making task ...
... An important premise of data mining is that databases are rich with hidden information which could be useful for decision making. The information we seek can be in many different forms and could be used for a verity of decision making tasks. An important type of decision making task ...
Lectures 10 Feed-Forward Neural Networks
... Further practical issues to do with Neural Network training In the last lecture we looked at coding the data in an appropriate way. Assumming we have managed to get the data coding correct what next? We will look at the internal working of the training next lecture - for now we will concentrate on p ...
... Further practical issues to do with Neural Network training In the last lecture we looked at coding the data in an appropriate way. Assumming we have managed to get the data coding correct what next? We will look at the internal working of the training next lecture - for now we will concentrate on p ...
Sathyabama Univarsity M.Tech May 2010 Data Mining and Data
... comparison is performed. Can class comparison mining be implemented efficiently using data cube techniques? ...
... comparison is performed. Can class comparison mining be implemented efficiently using data cube techniques? ...
10B28CI682: Data Mining Lab
... 1. Practical exposure on implementation of well known data mining tasks. 2. Exposure to real life data sets for analysis and prediction. 3. Learning performance evaluation of data mining algorithms in a supervised and an unsupervised setting. 4. Handling a small data mining project for a given pract ...
... 1. Practical exposure on implementation of well known data mining tasks. 2. Exposure to real life data sets for analysis and prediction. 3. Learning performance evaluation of data mining algorithms in a supervised and an unsupervised setting. 4. Handling a small data mining project for a given pract ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.