Assessing data mining results using swap randomization
... Classical methods • Hypothesis testing • Example: given two datasets C and D of real numbers, same number of observations • We want to test whether the means of these samples are ”significantly” different • Test statistic t = (E(C) - E(D))/s, where s is an estimate of the standard deviation • The t ...
... Classical methods • Hypothesis testing • Example: given two datasets C and D of real numbers, same number of observations • We want to test whether the means of these samples are ”significantly” different • Test statistic t = (E(C) - E(D))/s, where s is an estimate of the standard deviation • The t ...
chap5_alternative_classification
... K-nearest neighbors of a record x are data points that have the k smallest distance to x © Tan,Steinbach, Kumar ...
... K-nearest neighbors of a record x are data points that have the k smallest distance to x © Tan,Steinbach, Kumar ...
Gregory_DataForge_NADDI2013
... the same effort to learn and use a standard • But unless researchers are using DDI, the work has to be done by the archives and libraries where they deposit their data • Most research projects have lots of different proprietary tools, databases, and formats – The data is not easy to re-use across so ...
... the same effort to learn and use a standard • But unless researchers are using DDI, the work has to be done by the archives and libraries where they deposit their data • Most research projects have lots of different proprietary tools, databases, and formats – The data is not easy to re-use across so ...
A practitioner`s guide to resampling for data analysis, data mining
... to http://www.statcrunch.com/ for some data sets. This Web site is not related to the book; why not provide data sets dedicated to the book? Perhaps, the book simply aims to attract public to the author’s website. He has a consultancy company, which provides commercial statistics courses. This book ...
... to http://www.statcrunch.com/ for some data sets. This Web site is not related to the book; why not provide data sets dedicated to the book? Perhaps, the book simply aims to attract public to the author’s website. He has a consultancy company, which provides commercial statistics courses. This book ...
STAT 6289-G1 - The Department of Statistics
... Data mining is a multidisciplinary subject at the intersection of statistics, machine learning, visualization and computer science. This course is designed to introduce you to data mining techniques (automatic and semiautomatic) including predictive, descriptive and visualization modeling and their ...
... Data mining is a multidisciplinary subject at the intersection of statistics, machine learning, visualization and computer science. This course is designed to introduce you to data mining techniques (automatic and semiautomatic) including predictive, descriptive and visualization modeling and their ...
Document
... chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent sequences based on a static load-balancing. The static load-balancing is done by measuring the computatio ...
... chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent sequences based on a static load-balancing. The static load-balancing is done by measuring the computatio ...
crg weekly status report
... regular dataset. It was noticed that while removing the number of faults also removed the high weight of the attribute, it also increased the error rates in general by about 50%. Weka-grid was really not very stable and in many test it would not finish the task, and when it was able to finish the ta ...
... regular dataset. It was noticed that while removing the number of faults also removed the high weight of the attribute, it also increased the error rates in general by about 50%. Weka-grid was really not very stable and in many test it would not finish the task, and when it was able to finish the ta ...
Abstract - Chennaisunday.com
... Privacy-Preserving Data Analysis: The privacy preserving data analysis protocols assume that participating parties are truthful about their private input data . The techniques developed in assume that each party has an internal device that can verify whether they are telling the truth or not. In our ...
... Privacy-Preserving Data Analysis: The privacy preserving data analysis protocols assume that participating parties are truthful about their private input data . The techniques developed in assume that each party has an internal device that can verify whether they are telling the truth or not. In our ...
Educational Data Mining for Secondary and Higher
... proper procedure of analysis is the prerequisite to get valuable information from these raw data, which is known as Educational Data Mining (EDM). Educational Data Mining refers to the techniques, tools, and researches, designed for automatically extracting meaning from large repositories of data ge ...
... proper procedure of analysis is the prerequisite to get valuable information from these raw data, which is known as Educational Data Mining (EDM). Educational Data Mining refers to the techniques, tools, and researches, designed for automatically extracting meaning from large repositories of data ge ...
DP summary
... 3. Computational Complexity Look for appropriate modeling and solution strategies that can provide near-optimal decisions (good-enough) In the long run for the problem at hand. ...
... 3. Computational Complexity Look for appropriate modeling and solution strategies that can provide near-optimal decisions (good-enough) In the long run for the problem at hand. ...
GE 2110 - The State University of Zanzibar
... Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, typically metric: d(i, j) There is a separate “quality” function that measures the “goodness” of a cluster. The definitions of distance functions are usually very different for interval-scaled, boolean, categor ...
... Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, typically metric: d(i, j) There is a separate “quality” function that measures the “goodness” of a cluster. The definitions of distance functions are usually very different for interval-scaled, boolean, categor ...
Data Mining - Université catholique de Louvain
... 8. Piatetsky-Shapiro G. and W. J. Frawley (1991), "Knowledge Discovery in Databases", AAAI/MIT Press. 9. Piatetsky-Shapiro G., U. Fayyad, and P. Smith (1996). "From data mining to knowledge discovery: An overview", In U.M. Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, 1-35. ...
... 8. Piatetsky-Shapiro G. and W. J. Frawley (1991), "Knowledge Discovery in Databases", AAAI/MIT Press. 9. Piatetsky-Shapiro G., U. Fayyad, and P. Smith (1996). "From data mining to knowledge discovery: An overview", In U.M. Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, 1-35. ...
Semantic Data Preparation: The Instance-Selection plug-in
... A common background underlying database and ontology research is well known. Although ontologists and data modelers have been working together to bridge both areas, for example, in topics like conceptual modeling, database integration and metadata representation, less work has been undertaken in rel ...
... A common background underlying database and ontology research is well known. Although ontologists and data modelers have been working together to bridge both areas, for example, in topics like conceptual modeling, database integration and metadata representation, less work has been undertaken in rel ...
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.