28-WrapUp - EECS Instructional Support Group Home Page
... 2. Database Design/ER Models • Databases support many levels of abstraction – possible to design at abstract level in one form, store data in very different form • The E-R Model – Useful for design, easier for human to understand – Specify entities, attributes, relationships – Possible to convert E ...
... 2. Database Design/ER Models • Databases support many levels of abstraction – possible to design at abstract level in one form, store data in very different form • The E-R Model – Useful for design, easier for human to understand – Specify entities, attributes, relationships – Possible to convert E ...
Data Mining with SQL Server and R
... 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. Microsoft makes no warranties, express, implied or statutory, as to the informa ...
... 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. Microsoft makes no warranties, express, implied or statutory, as to the informa ...
DataMining_Overview - Computer Science | Furman University
... databases to find useful patterns” (Silberschatz) KDD – “Knowledge Discovery in Databases” “Attempts to discover rules and patterns from data” Discover Rules Make Predictions Areas of Use ...
... databases to find useful patterns” (Silberschatz) KDD – “Knowledge Discovery in Databases” “Attempts to discover rules and patterns from data” Discover Rules Make Predictions Areas of Use ...
Load Shedding using Transactional Algorithm in Data Stream
... Research problems and challenges that have been arisen in mining data streams can be solved to some extent using well established statistical and computational approaches. Here main focus is data and the task to be performed on data. When data is the key focus, idea is to examine only a subset of th ...
... Research problems and challenges that have been arisen in mining data streams can be solved to some extent using well established statistical and computational approaches. Here main focus is data and the task to be performed on data. When data is the key focus, idea is to examine only a subset of th ...
Unsupervised Learning - Bryn Mawr Computer Science
... Unsupervised Learning: Clustering Some material adapted from slides by Andrew Moore, CMU. Visit http://www.autonlab.org/tutorials/ for Andrew’s repository of Data Mining tutorials. ...
... Unsupervised Learning: Clustering Some material adapted from slides by Andrew Moore, CMU. Visit http://www.autonlab.org/tutorials/ for Andrew’s repository of Data Mining tutorials. ...
GenMiner: Mining non-redundant association rules from integrated
... names of the transcriptional regulator genes that bind its promoter regions. To take into account the hierarchical structure of GO, each gene is associated with all its annotations (direct and inherited). The resulting dataset was a matrix of 2465 lines representing yeast genes and 737 columns repre ...
... names of the transcriptional regulator genes that bind its promoter regions. To take into account the hierarchical structure of GO, each gene is associated with all its annotations (direct and inherited). The resulting dataset was a matrix of 2465 lines representing yeast genes and 737 columns repre ...
IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727 PP 53-57 www.iosrjournals.org
... confidence, reflecting how well they are matched with other records from different data sources. In applications that handle measurement data, e.g., sensor readings and distances to a query point, the data is inherently noisy, and is better represented by a probability distribution rather than a sin ...
... confidence, reflecting how well they are matched with other records from different data sources. In applications that handle measurement data, e.g., sensor readings and distances to a query point, the data is inherently noisy, and is better represented by a probability distribution rather than a sin ...
Multi-Assignment Clustering for Boolean Data
... (Kuhlmann et al., 2003). Since then, a number of combinatorial methods have been proposed that approximate a direct user-permission assignment matrix with roles as best possible, e.g. (Colantonio et al., 2008; Ene et al., 2008). Even though not originally designed for this application, we consider t ...
... (Kuhlmann et al., 2003). Since then, a number of combinatorial methods have been proposed that approximate a direct user-permission assignment matrix with roles as best possible, e.g. (Colantonio et al., 2008; Ene et al., 2008). Even though not originally designed for this application, we consider t ...
Can be cached across parallel operations
... In future work, plan to further extend this model: » More RDD transformations (e.g. shuffle) » More RDD persistence options (e.g. disk + memory) » Updatable RDDs (for incremental or streaming jobs) » Data sharing across applications ...
... In future work, plan to further extend this model: » More RDD transformations (e.g. shuffle) » More RDD persistence options (e.g. disk + memory) » Updatable RDDs (for incremental or streaming jobs) » Data sharing across applications ...
Time-series plot
... Data transformation • Many techniques for signal analysis require the data to be in the frequency domain • Usually data-independent transformations are used – The transformation matrix is determined a priori • E.g., discrete Fourier transform (DFT), discrete wavelet transform (DWT) – The distance b ...
... Data transformation • Many techniques for signal analysis require the data to be in the frequency domain • Usually data-independent transformations are used – The transformation matrix is determined a priori • E.g., discrete Fourier transform (DFT), discrete wavelet transform (DWT) – The distance b ...
Multi-Assignment Clustering for Boolean Data - ETH
... (Kuhlmann et al., 2003). Since then, a number of combinatorial methods have been proposed that approximate a direct user-permission assignment matrix with roles as best possible, e.g. (Colantonio et al., 2008; Ene et al., 2008). Even though not originally designed for this application, we consider t ...
... (Kuhlmann et al., 2003). Since then, a number of combinatorial methods have been proposed that approximate a direct user-permission assignment matrix with roles as best possible, e.g. (Colantonio et al., 2008; Ene et al., 2008). Even though not originally designed for this application, we consider t ...
Comparison of KEEL versus open source Data Mining tools: Knime
... It contains a large collection of evolutionary algorithms for predicting models, preprocessing methods (evolutionary feature and instance selection among others) and postprocessing procedures (evolutionary tuning of fuzzy rules). It also presents many state-of-the-art methods for different areas of ...
... It contains a large collection of evolutionary algorithms for predicting models, preprocessing methods (evolutionary feature and instance selection among others) and postprocessing procedures (evolutionary tuning of fuzzy rules). It also presents many state-of-the-art methods for different areas of ...
DMIN`15 The 2015International Conference on Data Mining
... WORLDCOMP'15 conferences). Submissions must be uploaded by March 31, 2015. Papers must not have been previously published or currently submitted for publication elsewhere. The length of the final/Camera-Ready papers (if accepted) will be limited to 7 (two-column IEEE style) pages. Each paper will be ...
... WORLDCOMP'15 conferences). Submissions must be uploaded by March 31, 2015. Papers must not have been previously published or currently submitted for publication elsewhere. The length of the final/Camera-Ready papers (if accepted) will be limited to 7 (two-column IEEE style) pages. Each paper will be ...
Data Methods - Indico
... • to analyse the avalanche of (big) social data, in order to gain insight and actionable knowledge SoBigData: Social Mining and Big Data Ecosystem ...
... • to analyse the avalanche of (big) social data, in order to gain insight and actionable knowledge SoBigData: Social Mining and Big Data Ecosystem ...
Classification Rules and Genetic Algorithm in Data
... A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solution to optimization and search problems. Genetic algorithms are categories as global search heuristics. Genetic algorithms are a probabilistic search and evolutionary optimization approach. Genetic alg ...
... A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solution to optimization and search problems. Genetic algorithms are categories as global search heuristics. Genetic algorithms are a probabilistic search and evolutionary optimization approach. Genetic alg ...
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.