A SAS Macro for Naive Bayes Classification
... [4,8,12,17] described and compared various methods of discretization of continuous features for NB classifiers and for other methods developed in the machine learning community. One of the simplest discretization methods, Equal Frequency Discretization (EFD), divides the sorted values of a continuou ...
... [4,8,12,17] described and compared various methods of discretization of continuous features for NB classifiers and for other methods developed in the machine learning community. One of the simplest discretization methods, Equal Frequency Discretization (EFD), divides the sorted values of a continuou ...
Predictive data mining for delinquency modeling
... From such matrix it is possible to extract a number of metrics to measure the performance of learning systems, such as Error rate (E) = (c+b) /(a+b+c+d) Accuracy (Acc) = (a+d) /(a+b+c+d) = 1 - E. The error rate (E) and the accuracy (Acc) are widely used metrics for measuring the performance of lear ...
... From such matrix it is possible to extract a number of metrics to measure the performance of learning systems, such as Error rate (E) = (c+b) /(a+b+c+d) Accuracy (Acc) = (a+d) /(a+b+c+d) = 1 - E. The error rate (E) and the accuracy (Acc) are widely used metrics for measuring the performance of lear ...
Principles of Data Mining - CEDAR
... • Make a statement about restricted regions of space spanned by variables • E.g.1: if X > thresh1 then Prob (Y > thresh2) =p • E.g.2: certain classes of transactions do not show peaks and troughs (bank discovers dead peopleʼs open accounts) ...
... • Make a statement about restricted regions of space spanned by variables • E.g.1: if X > thresh1 then Prob (Y > thresh2) =p • E.g.2: certain classes of transactions do not show peaks and troughs (bank discovers dead peopleʼs open accounts) ...
Finding Hidden Intelligence with Predictive Analysis of Data Mining
... © 2010 Microsoft Corporation & Project Botticelli Ltd. All rights reserved. The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Mic ...
... © 2010 Microsoft Corporation & Project Botticelli Ltd. All rights reserved. The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Mic ...
discovery of teleconnections using data mining technologies in
... remote sensing technologies, and other data acquisition systems. Traditional analysis methods of earth science data are not good enough. The main statistical methods, such as RPCA (Rotated Principal Component Analysis) and SVD (Singular Value Decomposition), have been used to discover teleconnection ...
... remote sensing technologies, and other data acquisition systems. Traditional analysis methods of earth science data are not good enough. The main statistical methods, such as RPCA (Rotated Principal Component Analysis) and SVD (Singular Value Decomposition), have been used to discover teleconnection ...
DAME - National e
... • Set of tools to build fast pattern recognition systems • Aimed at unstructured data • Aimed at large datasets • Scaleable technology 22 Oct 2001 ...
... • Set of tools to build fast pattern recognition systems • Aimed at unstructured data • Aimed at large datasets • Scaleable technology 22 Oct 2001 ...
Lecture 10
... data mining techniques we have discussed so far have focused on the classification, prediction or characterization of single data points, e.g.: Assign a record to one of a set of classes » Decision trees, back-propagation neural networks, Bayesian classifiers, etc. Predicting the value of a field ...
... data mining techniques we have discussed so far have focused on the classification, prediction or characterization of single data points, e.g.: Assign a record to one of a set of classes » Decision trees, back-propagation neural networks, Bayesian classifiers, etc. Predicting the value of a field ...
Clinical Decision Support Systems for Heart Disease Using Data
... frequently involves multiple, often concurrent, elements. This complexity makes treatment recommendation from data mining very difficult. Data mining is suited to assist decision making when many variables must be assessed, such as multiple concurrent treatments, but usually to make a single selecti ...
... frequently involves multiple, often concurrent, elements. This complexity makes treatment recommendation from data mining very difficult. Data mining is suited to assist decision making when many variables must be assessed, such as multiple concurrent treatments, but usually to make a single selecti ...
The Role of Hubness in Clustering High-Dimensional Data
... points tend to become harder to distinguish as dimensionality increases, which can cause problems with distance-based algorithms [6], [7], [8], [9]. The difficulties in dealing with high-dimensional data are omnipresent and abundant. However, not all phenomena which arise are necessarily detrimental ...
... points tend to become harder to distinguish as dimensionality increases, which can cause problems with distance-based algorithms [6], [7], [8], [9]. The difficulties in dealing with high-dimensional data are omnipresent and abundant. However, not all phenomena which arise are necessarily detrimental ...
Spatial Outlier Detection
... A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. The purpos ...
... A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. The purpos ...
A Clustering Internet Search Agent for User Assistance
... document representation and clustering algorithm. Document clustering algorithms perform several preprocessing steps including stop words removal and stemming on the documents collection. Document representation can be performed by several ways. One of the most used document representations is the v ...
... document representation and clustering algorithm. Document clustering algorithms perform several preprocessing steps including stop words removal and stemming on the documents collection. Document representation can be performed by several ways. One of the most used document representations is the v ...
WEKA: A Dynamic Software Suit for Machine
... processed. Very large datasets are typically split into several ...
... processed. Very large datasets are typically split into several ...
Tutorial_a_multi-sensor-data-fusion
... Build a Data Fusion System as a distributed assembly of fusion nodes ...
... Build a Data Fusion System as a distributed assembly of fusion nodes ...
Cluster analysis or clustering is a common technique for
... regarded as a region in which the density of data objects exceeds a threshold. DBSCAN and SSN are two typical algorithms of this kind. DBSCAN algorithm The DBSCAN algorithm was first introduced by Ester, and relies on a density-based notion of clusters. Clusters are identified by looking at the dens ...
... regarded as a region in which the density of data objects exceeds a threshold. DBSCAN and SSN are two typical algorithms of this kind. DBSCAN algorithm The DBSCAN algorithm was first introduced by Ester, and relies on a density-based notion of clusters. Clusters are identified by looking at the dens ...
Anomaly Detection
... – Compute the distance between every pair of data points – There are various ways to define outliers: Data ...
... – Compute the distance between every pair of data points – There are various ways to define outliers: Data ...
Decision Support System for predicting Football Game result
... After this work it was possible to find a path in order to achieve the defined goals. In this first step logical blocks were developed combined with data mining models in order to predict the better bet. In terms of model assessment, the results were not totally satisfactory being notorious the need ...
... After this work it was possible to find a path in order to achieve the defined goals. In this first step logical blocks were developed combined with data mining models in order to predict the better bet. In terms of model assessment, the results were not totally satisfactory being notorious the need ...
Example: Data Mining for the NBA
... - Are there techniques analogous to techniques in approximate query processing ...
... - Are there techniques analogous to techniques in approximate query processing ...
Document
... that best improves the “purity” of the training set examples – The initial training set has a mixture of instances from different classes and is thus relatively impure – E.g. if degree exactly predicts credit risk, partitioning on degree would result in each child having instances of only one class ...
... that best improves the “purity” of the training set examples – The initial training set has a mixture of instances from different classes and is thus relatively impure – E.g. if degree exactly predicts credit risk, partitioning on degree would result in each child having instances of only one class ...
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.