A tutorial on using the rminer R package for data mining tasks*
... The rminer package (http://cran.r-project.org/web/packages/rminer/ index.html) goal is to provide a reduced and coherent set of R functions to perform classification and regression. The package is particularly suited for non R expert users, as it allows to perform the full data mining process using ...
... The rminer package (http://cran.r-project.org/web/packages/rminer/ index.html) goal is to provide a reduced and coherent set of R functions to perform classification and regression. The package is particularly suited for non R expert users, as it allows to perform the full data mining process using ...
Knowledge Discovery System For Cost
... COMAD 2005b, Hyderabad, India, December 20–22, 2005 ©Computer Society of India, 2005 ...
... COMAD 2005b, Hyderabad, India, December 20–22, 2005 ©Computer Society of India, 2005 ...
On the effect of data set size on bias and variance in classification
... Variance measures the degree to which the predictions of the classifiers developed by a learning algorithm differ from training sample to training sample. When sample sizes are small, the relative impact of sampling on the general composition of a sample can be expected to be large. For example, if ...
... Variance measures the degree to which the predictions of the classifiers developed by a learning algorithm differ from training sample to training sample. When sample sizes are small, the relative impact of sampling on the general composition of a sample can be expected to be large. For example, if ...
Mining Sensor Streams for Discovering Human Activity
... into a prefix tree. They also designed methods for regressionbased stream mining algorithms [26]. More recent approaches have introduced methods for managing the history of items over time [13], [26]. The main idea is that one usually is more interested in recent changes in more detail, while older ...
... into a prefix tree. They also designed methods for regressionbased stream mining algorithms [26]. More recent approaches have introduced methods for managing the history of items over time [13], [26]. The main idea is that one usually is more interested in recent changes in more detail, while older ...
Pattern Recognition Algorithms for Cluster
... number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each ...
... number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each ...
A Review of Class Imbalance Problem
... algorithms. The standard evaluation metrics used are accuracy and error rate however, these metrics are not proper to handle imbalance classes as the overall accuracy be biased to the majority class regardless of the minority class with lower samples which leads to poor performance on it. For the tw ...
... algorithms. The standard evaluation metrics used are accuracy and error rate however, these metrics are not proper to handle imbalance classes as the overall accuracy be biased to the majority class regardless of the minority class with lower samples which leads to poor performance on it. For the tw ...
Measuring Time Series` Similarity through Large Singular Features
... class of linear transformations like (amplitude, time) rescaling, addition of linear trend or constant bias. This is understandable since most such operations affect the parameter values of commonly used estimators (e.g. power spectrum), or destroy any stationarity potentially present in the time se ...
... class of linear transformations like (amplitude, time) rescaling, addition of linear trend or constant bias. This is understandable since most such operations affect the parameter values of commonly used estimators (e.g. power spectrum), or destroy any stationarity potentially present in the time se ...
A Dynamic Knowledge Base - K
... store, access and analyse domain independent dynamic information. Such specification will include the definition of the dynamic KB which will be a generalization of the one described in WP3, with additional information including time attributes of the maintained knowledge. In particular tasks relate ...
... store, access and analyse domain independent dynamic information. Such specification will include the definition of the dynamic KB which will be a generalization of the one described in WP3, with additional information including time attributes of the maintained knowledge. In particular tasks relate ...
Subspace Memory Clustering
... In this section we present comparison of our algorithm, which we denote by SuMC (Subspace Memory Clustering), with classical approach given by ORCLUS. Both of these methods detect clusters in arbitrarily-oriented subspaces and are able to find dimension of each component. Moreover, these algorithms ...
... In this section we present comparison of our algorithm, which we denote by SuMC (Subspace Memory Clustering), with classical approach given by ORCLUS. Both of these methods detect clusters in arbitrarily-oriented subspaces and are able to find dimension of each component. Moreover, these algorithms ...
Poster/Demo Presentation Index
... Integrative Representation and Analysis of the LINCS Cell Lines Using the Cell Line Ontology Caty Chung ...
... Integrative Representation and Analysis of the LINCS Cell Lines Using the Cell Line Ontology Caty Chung ...
Algorithms and Software for Collaborative Discovery from
... a centralized location for analysis. Hence, there is a need for efficient algorithms for learning from multiple distributed data sources without the need to transmit large amounts of data. In other domains, the ability of autonomous organizations to share raw data may be limited due to a variety of ...
... a centralized location for analysis. Hence, there is a need for efficient algorithms for learning from multiple distributed data sources without the need to transmit large amounts of data. In other domains, the ability of autonomous organizations to share raw data may be limited due to a variety of ...
PDF (free)
... focuses on smuggling of vessels. The data source is the complete record of fishing vessels leaving and returning to ports in the Taiwan region. This paper essay applies both artificial neural networks (ANN) and logistics regression (LR) to classify and predict criminal behaviors in smuggling. At the ...
... focuses on smuggling of vessels. The data source is the complete record of fishing vessels leaving and returning to ports in the Taiwan region. This paper essay applies both artificial neural networks (ANN) and logistics regression (LR) to classify and predict criminal behaviors in smuggling. At the ...
View Sample PDF
... according to the density of the neighborhood objects (an approach adopted by DBSCAN), or according to some density function (such that used by DENCLUE). A grid-based method first quantizes the object space into a finite number of cells thus forming a grid structure, and then performs clustering on t ...
... according to the density of the neighborhood objects (an approach adopted by DBSCAN), or according to some density function (such that used by DENCLUE). A grid-based method first quantizes the object space into a finite number of cells thus forming a grid structure, and then performs clustering on t ...
The Impact of Sample Reduction on PCA-based Feature Mykola Pechenizkiy Seppo Puuronen
... experimental study and say that, although being different in nature, stratified sampling and kd-tree based sampling have similar effect with respect to the application of FE for NB classification. This fact is the main motivation to try the combination of these approaches, so that both class informa ...
... experimental study and say that, although being different in nature, stratified sampling and kd-tree based sampling have similar effect with respect to the application of FE for NB classification. This fact is the main motivation to try the combination of these approaches, so that both class informa ...
PDF version - PCP-net
... To further illustrate the application of FCA to grid data, we consider the use of repertory grids to explore reasons why people get tattoos. Consider the grid from a 57 year old man with approximately ten tattoos. After obtaining his first tattoo at the age of 19, this man has continued to have tatt ...
... To further illustrate the application of FCA to grid data, we consider the use of repertory grids to explore reasons why people get tattoos. Consider the grid from a 57 year old man with approximately ten tattoos. After obtaining his first tattoo at the age of 19, this man has continued to have tatt ...
Data Mining examples
... Information is measured in bits Given a probability distribution, the info required to predict an event is the distribution’s entropy Entropy gives the information required in bits (this can involve fractions of bits!) ...
... Information is measured in bits Given a probability distribution, the info required to predict an event is the distribution’s entropy Entropy gives the information required in bits (this can involve fractions of bits!) ...
CL4201593597
... traditional crawling algorithms to find data relevant to the search query. But most of the times it returns irrelevant data as well which becomes confusing for the user. In a normal XML data the user inputs the search query in terms of a keyword or a question and the answer to the search query shoul ...
... traditional crawling algorithms to find data relevant to the search query. But most of the times it returns irrelevant data as well which becomes confusing for the user. In a normal XML data the user inputs the search query in terms of a keyword or a question and the answer to the search query shoul ...
Expert Systems
... Knowledge-based expert systems or simply expert systems An expert system is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain (Wikipedia) Use human knowledge to solve problems that normally would require human intellige ...
... Knowledge-based expert systems or simply expert systems An expert system is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain (Wikipedia) Use human knowledge to solve problems that normally would require human intellige ...
business intelligence and analytics
... » APPLICATION CASE 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network 55 Analytics Applied to Different Domains 56 Analytics or Data Science? 56 1.9 Brief Introduction to Big Data Analytics 57 What Is Big Data? 57 » APPLICATION CASE 1.7 Gilt Groupe's ...
... » APPLICATION CASE 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network 55 Analytics Applied to Different Domains 56 Analytics or Data Science? 56 1.9 Brief Introduction to Big Data Analytics 57 What Is Big Data? 57 » APPLICATION CASE 1.7 Gilt Groupe's ...
Multidimensional database representation of
... constituents. For instance, a dimension representing geographical locations would consist of geographical sites, such as cities, as its members. This fundamental property of multidimensional databases can be used to effectively describe real-world characteristics using a set of dimensions. Consider ...
... constituents. For instance, a dimension representing geographical locations would consist of geographical sites, such as cities, as its members. This fundamental property of multidimensional databases can be used to effectively describe real-world characteristics using a set of dimensions. Consider ...
Context-Sensitive and Expectation-Guided Temporal Abstraction of High- Frequency Data
... process. A trend template defines disorders as typical patterns of relevant parameters.Thesepatterns consist of a partially ordered set of temporal intervals with uncertain endpoints. The trend templates are used to detect trends in series of time-stampeddata. The drawbacksof this approach lie in th ...
... process. A trend template defines disorders as typical patterns of relevant parameters.Thesepatterns consist of a partially ordered set of temporal intervals with uncertain endpoints. The trend templates are used to detect trends in series of time-stampeddata. The drawbacksof this approach lie in th ...
Vasant Dhar
... 52. Dhar, V., and Jarke, M., Using Teleological Design Knowledge for Large Systems Development and Maintenance, Sixth International Workshop on Expert Systems, Avignon, France, April 1986. 53. Dhar, V., On the Plausibility and Scope of Expert Systems in Management, Nineteenth Hawaii International Co ...
... 52. Dhar, V., and Jarke, M., Using Teleological Design Knowledge for Large Systems Development and Maintenance, Sixth International Workshop on Expert Systems, Avignon, France, April 1986. 53. Dhar, V., On the Plausibility and Scope of Expert Systems in Management, Nineteenth Hawaii International Co ...
What`s Hot in Intelligent User Interfaces
... Affective computing is a component in developing intelligent user interfaces that are capable of detecting and responding to the affective needs of users. Since human emotions are often expressed via speech, facial expressions, body postures and physiological signs (e.g., skin conductivity, pupillar ...
... Affective computing is a component in developing intelligent user interfaces that are capable of detecting and responding to the affective needs of users. Since human emotions are often expressed via speech, facial expressions, body postures and physiological signs (e.g., skin conductivity, pupillar ...
Full text
... tasks that can be performed using machine learning or data mining techniques. Hence a lot of research has been carried out in this area. Chen et al. [7] use a two-stage approach composed of k-means clustering and support vector machines (SVM) classification together with computation of feature impor ...
... tasks that can be performed using machine learning or data mining techniques. Hence a lot of research has been carried out in this area. Chen et al. [7] use a two-stage approach composed of k-means clustering and support vector machines (SVM) classification together with computation of feature impor ...
Chapter 15 Databases for Decision Support Database Principles
... algorithms, neural networks, artificial intelligence, and other advanced modeling tools • Create actionable predictive models based on available data • Models are used in areas such as: – Customer relationships, customer service, customer retention, fraud detection, targeted marketing, and optimized ...
... algorithms, neural networks, artificial intelligence, and other advanced modeling tools • Create actionable predictive models based on available data • Models are used in areas such as: – Customer relationships, customer service, customer retention, fraud detection, targeted marketing, and optimized ...