DATABASE SYSTEMS Applying Data Mining Methods for the
... • Are two data sets compatible for a given data analysis task? • Is a subset of recorded attributes sufficient to represent the data's structure? • How can we measure importance and redundancy of a subset of features for a given task? ...
... • Are two data sets compatible for a given data analysis task? • Is a subset of recorded attributes sufficient to represent the data's structure? • How can we measure importance and redundancy of a subset of features for a given task? ...
Principles of Data Mining and Knowledge Discovery
... ' Exploration of Document Collections with Self-Organizing Maps: A Novel Approach to Similarity Representation R. Feldman, W. Klosgen, Y. Ben-Yehuda, G. Kedar, V. Reznikov Pattern Based Browsing in Document Collections ...
... ' Exploration of Document Collections with Self-Organizing Maps: A Novel Approach to Similarity Representation R. Feldman, W. Klosgen, Y. Ben-Yehuda, G. Kedar, V. Reznikov Pattern Based Browsing in Document Collections ...
MTIS-2016 - UVic ECE - University of Victoria
... to discuss, exchange ideas, and present their research findings on data mining and applications. Topics of interest that MTIS-2016 covers include general data mining techniques and methodologies, as well as their applications (but are not limited to): ...
... to discuss, exchange ideas, and present their research findings on data mining and applications. Topics of interest that MTIS-2016 covers include general data mining techniques and methodologies, as well as their applications (but are not limited to): ...
Fact sheet
... achieved with millions of GPS coordinate points generated by daily activities, historical data from police reporting system, human resource management system, and along with data from unstructured sources with various volumes that could be integrated, processed quickly as a big data solution and vis ...
... achieved with millions of GPS coordinate points generated by daily activities, historical data from police reporting system, human resource management system, and along with data from unstructured sources with various volumes that could be integrated, processed quickly as a big data solution and vis ...
03vertical_audio0 - NDSU Computer Science
... Marketing of Data Warehouses was so successful, nobody noticed the failure! (or seem to mind paying double) Most enterprises now have a separate DW from their DBMS ...
... Marketing of Data Warehouses was so successful, nobody noticed the failure! (or seem to mind paying double) Most enterprises now have a separate DW from their DBMS ...
A Systemic Artificial Intelligence (AI) Approach
... Artificial Intelligence (AI), in a broad sense spanning over machine learning, big data, bio-inspired computing (neural networks, evolutionary computing). Often ”computational intelligence” is more appropriate ...
... Artificial Intelligence (AI), in a broad sense spanning over machine learning, big data, bio-inspired computing (neural networks, evolutionary computing). Often ”computational intelligence” is more appropriate ...
DataScience
... unpack the need for cross-functional data strategies in order to develop sustainable competitive advantage. Module 6: Idea Tournament: each participant submits a cumulative data proposal and estimates the value that realized approach to data could bring to your organization. Proposals are evaluated ...
... unpack the need for cross-functional data strategies in order to develop sustainable competitive advantage. Module 6: Idea Tournament: each participant submits a cumulative data proposal and estimates the value that realized approach to data could bring to your organization. Proposals are evaluated ...
Abstract - PG Embedded systems
... developed for such data, we may mention principal component analysis clustering linear regression and multidimensional scaling. Probability distributions, intervals, and possibility distributions may be seen as three instances of a more general model, in which data uncertainty is expressed by means ...
... developed for such data, we may mention principal component analysis clustering linear regression and multidimensional scaling. Probability distributions, intervals, and possibility distributions may be seen as three instances of a more general model, in which data uncertainty is expressed by means ...
Data Mining
... • The goal of a data warehouse is to support decision making with data. • Data Mining can be used in conjunction with a data warehouse to help with certain types of decisions ...
... • The goal of a data warehouse is to support decision making with data. • Data Mining can be used in conjunction with a data warehouse to help with certain types of decisions ...
Clustering and Classification of Infrared Hyperspectral Aerial images
... plants affected by the CO2 and those stressed by other factors [1]. The hyperspectral data of the test plot are in 488 bands from 400 to 2500nm [2]. They are georeferenced and resampled onto a 0.5m resolution grid [1]. Here we report an application of Data Mining techniques [3] for a deeper analy ...
... plants affected by the CO2 and those stressed by other factors [1]. The hyperspectral data of the test plot are in 488 bands from 400 to 2500nm [2]. They are georeferenced and resampled onto a 0.5m resolution grid [1]. Here we report an application of Data Mining techniques [3] for a deeper analy ...
Introduction to Machine Learning
... How many if are necessary to select the correct level? How many time is necessary to study the relations between the hierarchy and attributes? ...
... How many if are necessary to select the correct level? How many time is necessary to study the relations between the hierarchy and attributes? ...
Data Mining & Machine Learning Group
... the values are of type double and NominalDataset where all the values are of type int where each integer value is mapped to a value of a nominal attribute. We have a high level interface for Dataset and so one can write code using this interface and switching from one type of dataset to another type ...
... the values are of type double and NominalDataset where all the values are of type int where each integer value is mapped to a value of a nominal attribute. We have a high level interface for Dataset and so one can write code using this interface and switching from one type of dataset to another type ...
A Clear Vision - Sabre Airline Solutions
... tool may be a goal, in many cases, this goal cannot be achieved because of previous purchase decisions, business unit independence, cost restrictions or tool capabilities. ...
... tool may be a goal, in many cases, this goal cannot be achieved because of previous purchase decisions, business unit independence, cost restrictions or tool capabilities. ...
extended abstract
... seasonal effects can be short-term and long-term. Methods like Holt-Winter have been developed to deal with trend detection while consider seasonal component. anomalies and spam can be caused by data transmission errors or targeted violation, thus the these generally errors need to be identified ...
... seasonal effects can be short-term and long-term. Methods like Holt-Winter have been developed to deal with trend detection while consider seasonal component. anomalies and spam can be caused by data transmission errors or targeted violation, thus the these generally errors need to be identified ...
Data Mining in EDA and Test – Principles, Opportunities
... to explain how an EDA/test problem can be formulated to facilitate the application of these learning techniques. Opportunities for applying data mining include problems in areas such as functional verification, simulation trace analysis, layout analysis, timing analysis, designsilicon correlation, F ...
... to explain how an EDA/test problem can be formulated to facilitate the application of these learning techniques. Opportunities for applying data mining include problems in areas such as functional verification, simulation trace analysis, layout analysis, timing analysis, designsilicon correlation, F ...
Big Data Infrastructure
... Binary classifiers form a primitive building block for multiclass problems ...
... Binary classifiers form a primitive building block for multiclass problems ...
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.