LISp-Miner - Demonstration overview
... Demonstration of the LISp-Miner system covers the following: • LISp-Miner application to your own data. We recommend to use the following demonstration examples. • Description and installation of data sets that are used in demonstration examples of the particular KDD procedures. There are two such d ...
... Demonstration of the LISp-Miner system covers the following: • LISp-Miner application to your own data. We recommend to use the following demonstration examples. • Description and installation of data sets that are used in demonstration examples of the particular KDD procedures. There are two such d ...
introduction
... • Instead of writing a program by hand, we collect lots of examples that specify the correct output for a given input. • A machine learning algorithm then takes these examples and produces a program that does the job. – The program produced by the learning algorithm may look very different from a ty ...
... • Instead of writing a program by hand, we collect lots of examples that specify the correct output for a given input. • A machine learning algorithm then takes these examples and produces a program that does the job. – The program produced by the learning algorithm may look very different from a ty ...
Data Mining
... Matching & search: finding instances similar to x Clustering: discovering groups of similar instances Association rule extraction: if a & b then c Summarization: summarizing group descriptions Link detection: finding relationships ...
... Matching & search: finding instances similar to x Clustering: discovering groups of similar instances Association rule extraction: if a & b then c Summarization: summarizing group descriptions Link detection: finding relationships ...
A Systematic Overview of Data Mining Algorithms
... • e.g., search based on steepest descent is a computational method- for it to be an algorithm need to specify where to begin, how to calculate direction of descent, when to terminate search ...
... • e.g., search based on steepest descent is a computational method- for it to be an algorithm need to specify where to begin, how to calculate direction of descent, when to terminate search ...
Haiku: interactive comprehensible data mining
... elasticity, and so on. Multiple elements can affect one parameter, or a subset of parameters can be chosen. Nodes are scattered randomly into the 3d space, with their associated links. This 3d space has obeys a set of physicaltype laws, which affect this initial arrangement. Links tend to want to as ...
... elasticity, and so on. Multiple elements can affect one parameter, or a subset of parameters can be chosen. Nodes are scattered randomly into the 3d space, with their associated links. This 3d space has obeys a set of physicaltype laws, which affect this initial arrangement. Links tend to want to as ...
Assignement 3
... parameters; in particular how you’ve chosen the values K1, K2 for k-means. Plot figures by using different colors or different markers to show what cluster each data point belongs. Explain the differences of the two datasets based on the results of the clustering. Finally, give a suggestion how a k- ...
... parameters; in particular how you’ve chosen the values K1, K2 for k-means. Plot figures by using different colors or different markers to show what cluster each data point belongs. Explain the differences of the two datasets based on the results of the clustering. Finally, give a suggestion how a k- ...
Lecture 13
... - Iterative approach : select genes under different pvalue cutoff, then select the one with good performance using cross-validation. - Principal components (pro and con). - Discriminant analysis (e.g., LDA). ...
... - Iterative approach : select genes under different pvalue cutoff, then select the one with good performance using cross-validation. - Principal components (pro and con). - Discriminant analysis (e.g., LDA). ...
by downloading our brochure. - PharmaCommunications Group Inc.
... consumer healthcare product can be tricky. To best position your brand, a variety of factors come into play. • Who is your target demographic and what drives their purchasing decisions? • Why do HCPs prescribe one medication over another? • What characteristics are the most accurate indicators of ...
... consumer healthcare product can be tricky. To best position your brand, a variety of factors come into play. • Who is your target demographic and what drives their purchasing decisions? • Why do HCPs prescribe one medication over another? • What characteristics are the most accurate indicators of ...
Geoscientific Data Model
... • Cyclone tracks - trajectories traveled along low-pressure areas over time, that can be extracted from a sea-level pressure dataset • Data mining in business applications and Geoscientific feature extraction involve sieving through large volumes of isolated events and data to locate salient pattern ...
... • Cyclone tracks - trajectories traveled along low-pressure areas over time, that can be extracted from a sea-level pressure dataset • Data mining in business applications and Geoscientific feature extraction involve sieving through large volumes of isolated events and data to locate salient pattern ...
Proposal Presentation
... Investigating mobile based prediction modelling of academic performance for primary school pupils: a data mining approach. ...
... Investigating mobile based prediction modelling of academic performance for primary school pupils: a data mining approach. ...
CPSC 340: Data Mining Machine Learning
... Big data phenomenon and types of data. Definitions of data mining and machine learning. Applications and impact. ...
... Big data phenomenon and types of data. Definitions of data mining and machine learning. Applications and impact. ...
CS 636 Computer Vision
... – Kernel-based framework is very powerful, flexible – SVMs work very well in practice, even with very small training sample sizes ...
... – Kernel-based framework is very powerful, flexible – SVMs work very well in practice, even with very small training sample sizes ...
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.