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Brief Description of SAS Products
... built around the four primary data-driven tasks common to any application: data access, data management, data analysis and data presentation. SAS/IntrNet integrates the SAS System and the World Wide Web. It provides both Common Gateway Interface (CGI) and Java technologies for building dynamic Web a ...
... built around the four primary data-driven tasks common to any application: data access, data management, data analysis and data presentation. SAS/IntrNet integrates the SAS System and the World Wide Web. It provides both Common Gateway Interface (CGI) and Java technologies for building dynamic Web a ...
Multi-Step Density-Based Clustering
... Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex distance measures are first choice but also simpler distance functions are available which can be computed much more efficien ...
... Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex distance measures are first choice but also simpler distance functions are available which can be computed much more efficien ...
Detecting Time Correlations in Time-Series Data Streams
... describe our method for detecting time-correlations for a pair of time-series data streams. Then, we describe how a set of timeseries data streams can be merged into one compact time-series data stream. As a result of the merging operation, we reduce the problem of finding time-correlations between ...
... describe our method for detecting time-correlations for a pair of time-series data streams. Then, we describe how a set of timeseries data streams can be merged into one compact time-series data stream. As a result of the merging operation, we reduce the problem of finding time-correlations between ...
Mining Educational Data to Analyze Students
... Algorithm, Nearest Neighbor method etc., are used for knowledge discovery from databases. These techniques and methods in data mining need brief mention to have better understanding. A. Classification Classification is the most commonly applied data mining technique, which employs a set of pre-class ...
... Algorithm, Nearest Neighbor method etc., are used for knowledge discovery from databases. These techniques and methods in data mining need brief mention to have better understanding. A. Classification Classification is the most commonly applied data mining technique, which employs a set of pre-class ...
Modern Methods of Statistical Learning sf2935 Lecture 16
... This algorithm has as an input a predefined number of clusters, that is the k in its name. Means stands for an average, an average location of all the members of a particular cluster. The value of each attribute of an example represents a distance of the example from the origin along the attribute a ...
... This algorithm has as an input a predefined number of clusters, that is the k in its name. Means stands for an average, an average location of all the members of a particular cluster. The value of each attribute of an example represents a distance of the example from the origin along the attribute a ...
Optimizing Online Spatial Data Analysis with Sequential Query
... Fig. 2: The MapQL query result on real property data is displayed on the map. Comparing with using GIS application programming interface (API), MapQL provides a better interface to facilitate the use of TerraFly map for both developers and end users without any functionality limitation. Similar to G ...
... Fig. 2: The MapQL query result on real property data is displayed on the map. Comparing with using GIS application programming interface (API), MapQL provides a better interface to facilitate the use of TerraFly map for both developers and end users without any functionality limitation. Similar to G ...
Data Mining Deployment for High-ROI Predictive Analytics
... Rapid modeling accelerates time-to-value Soft costs, such as the time cost of a delay in deploying data mining results, should also be factored into data mining ROI calculations. For example, what is the time cost of a 30-day delay in deploying a customer retention model that is predicted to save 2, ...
... Rapid modeling accelerates time-to-value Soft costs, such as the time cost of a delay in deploying data mining results, should also be factored into data mining ROI calculations. For example, what is the time cost of a 30-day delay in deploying a customer retention model that is predicted to save 2, ...
Service-Oriented Data Mining
... and the adoption of universally accepted technologies, including XML, SOAP, WSDL and UDDI. The most important implementation of SOA is represented by web services. Web servicebased SOAs are now widely accepted for on-demand computing as well as for developing more interoperable systems. They provide ...
... and the adoption of universally accepted technologies, including XML, SOAP, WSDL and UDDI. The most important implementation of SOA is represented by web services. Web servicebased SOAs are now widely accepted for on-demand computing as well as for developing more interoperable systems. They provide ...
Subspace Clustering using CLIQUE: An Exploratory Study
... Subspace clustering is the next step of traditional clustering, it solve the problems of clustering high dimensional data by combining density and grid based traditional clustering methods and seeks to find clusters in different subspaces within a dataset [8]. It mine clusters in high dimensional da ...
... Subspace clustering is the next step of traditional clustering, it solve the problems of clustering high dimensional data by combining density and grid based traditional clustering methods and seeks to find clusters in different subspaces within a dataset [8]. It mine clusters in high dimensional da ...
Chameleon: Hierarchical Clustering Using Dynamic Modeling
... Relative closeness. Relative closeness involves concepts that are analogous to those developed for relative interconnectivity. The absolute closeness of clusters is the average weight (as opposed to the sum of weights for interconnectivity) of the edges that connect vertices in Ci to those in Cj. Si ...
... Relative closeness. Relative closeness involves concepts that are analogous to those developed for relative interconnectivity. The absolute closeness of clusters is the average weight (as opposed to the sum of weights for interconnectivity) of the edges that connect vertices in Ci to those in Cj. Si ...
Accuracy - classification task 1
... more analysis. The experts who handle the transactions do these manual tasks (of handling daily transactions by analyzing) repetitively. Taking into consideration the company have hundreds of clients, handling numerous transactions every day is very tedious and labor-intensive, time consuming and co ...
... more analysis. The experts who handle the transactions do these manual tasks (of handling daily transactions by analyzing) repetitively. Taking into consideration the company have hundreds of clients, handling numerous transactions every day is very tedious and labor-intensive, time consuming and co ...
1-p
... – For example, the single-link clustering algorithm works well on data sets containing non-isotropic (non-roundish) clusters including wellseparated, chain-like, and concentric clusters, whereas a typical partitional algorithm such as the k-means algorithm works well only on data sets having isotrop ...
... – For example, the single-link clustering algorithm works well on data sets containing non-isotropic (non-roundish) clusters including wellseparated, chain-like, and concentric clusters, whereas a typical partitional algorithm such as the k-means algorithm works well only on data sets having isotrop ...
A Powerpoint presentation on Clustering
... – For example, the single-link clustering algorithm works well on data sets containing non-isotropic (non-roundish) clusters including wellseparated, chain-like, and concentric clusters, whereas a typical partitional algorithm such as the k-means algorithm works well only on data sets having isotrop ...
... – For example, the single-link clustering algorithm works well on data sets containing non-isotropic (non-roundish) clusters including wellseparated, chain-like, and concentric clusters, whereas a typical partitional algorithm such as the k-means algorithm works well only on data sets having isotrop ...
- UUM Electronic Theses and Dissertation
... real-world applications (Andrews & Fox, 2007). Text clustering means finding the groups that are related to each other. These groups are collected together in an unstructured formal document. In fact, clustering becomes very famous for its ability to offer an exceptional way of digesting in addition ...
... real-world applications (Andrews & Fox, 2007). Text clustering means finding the groups that are related to each other. These groups are collected together in an unstructured formal document. In fact, clustering becomes very famous for its ability to offer an exceptional way of digesting in addition ...
Introduction: Lessons Learned from Data Mining Applications and
... points were available for other variables that were needed to determine the relations of interest. In another effort that involved modeling gene regulation, there were thousands of measurements, since DNA microarrays can estimate expression levels for many genes at the same time, but there were only ...
... points were available for other variables that were needed to determine the relations of interest. In another effort that involved modeling gene regulation, there were thousands of measurements, since DNA microarrays can estimate expression levels for many genes at the same time, but there were only ...
December 2010 January 2011 February 2011
... It is well accepted that India is the most observed country and has been making great impact on use of ICT in the Global market. Most advanced sectors of ICT are depending upon the brains of Indian youth. As we are entering in the second decade of the 21st century, the challenges of the Indian dream ...
... It is well accepted that India is the most observed country and has been making great impact on use of ICT in the Global market. Most advanced sectors of ICT are depending upon the brains of Indian youth. As we are entering in the second decade of the 21st century, the challenges of the Indian dream ...
Clustering - Computer Science and Engineering
... closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster ...
... closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster ...
Nonlinear dimensionality reduction
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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.