using data mining techniques in e-learning environment by anamika
... Intelligent Tutoring Systems (ITS). Various data such as navigation sequence, data related to student learning and assessment is generated by the usage of eLearning systems. This data such as resource usage and performance data can be mined to find some hidden patterns. The result of mining process ...
... Intelligent Tutoring Systems (ITS). Various data such as navigation sequence, data related to student learning and assessment is generated by the usage of eLearning systems. This data such as resource usage and performance data can be mined to find some hidden patterns. The result of mining process ...
Jieping - Arizona State University
... – Massive data compression using tensor SVD – Clustering and classification of Microarray gene expression data – Gene expression pattern image classification and retrieval ...
... – Massive data compression using tensor SVD – Clustering and classification of Microarray gene expression data – Gene expression pattern image classification and retrieval ...
3rd Edition: Chapter 1
... It is important to know the main concepts, techniques, mechanisms, and algorithms. It is important to know how to explain them using examples. It is important to have a big picture on the Internet. ...
... It is important to know the main concepts, techniques, mechanisms, and algorithms. It is important to know how to explain them using examples. It is important to have a big picture on the Internet. ...
Educating the Total Force - Naval Postgraduate School
... • If Time Remains – Simple forecasting models ...
... • If Time Remains – Simple forecasting models ...
Data Mining and Bioinformatics Course Syllabus INSTRUCTORS
... • Feature selection – dimensionality reduction • Classification – decision-tree, Bayesian, rule-based, SVM, ensemble methods • Anomaly detection – statistics-based, density-based, clustering-based • Evaluation and validation of data mining results • Correlation analysis – metrics and analysis • Grap ...
... • Feature selection – dimensionality reduction • Classification – decision-tree, Bayesian, rule-based, SVM, ensemble methods • Anomaly detection – statistics-based, density-based, clustering-based • Evaluation and validation of data mining results • Correlation analysis – metrics and analysis • Grap ...
CS 561
... Apriori: frequent itemsets EM: Expedctation-Maximization PageRank AdaBoost kNN: k-Nearest Neighbor Naïve Bayes CART: Classification and Regression Trees Recitation sections Hands-on activities are critical components of computer science courses that have significant programming compone ...
... Apriori: frequent itemsets EM: Expedctation-Maximization PageRank AdaBoost kNN: k-Nearest Neighbor Naïve Bayes CART: Classification and Regression Trees Recitation sections Hands-on activities are critical components of computer science courses that have significant programming compone ...
KernelIntro
... •Kernel algorithms are typically constrained convex optimization problems solved with either spectral methods or convex optimization tools. • Efficient algorithms do exist in most cases. • The similarity to linear methods facilitates analysis. There are strong generalization bounds on test error. ...
... •Kernel algorithms are typically constrained convex optimization problems solved with either spectral methods or convex optimization tools. • Efficient algorithms do exist in most cases. • The similarity to linear methods facilitates analysis. There are strong generalization bounds on test error. ...
References
... --------K-anonymity -----------Pierangela Samarati: Protecting Respondents' Identities in Microdata Release. IEEE Trans. Knowl. Data Eng. 13(6): 1010-1027 (2001) Demo to play with : http://privacy.cs.cmu.edu/datafly/index.html k-anonymity: a model for protecting privacy. Sweeney, L. International Jo ...
... --------K-anonymity -----------Pierangela Samarati: Protecting Respondents' Identities in Microdata Release. IEEE Trans. Knowl. Data Eng. 13(6): 1010-1027 (2001) Demo to play with : http://privacy.cs.cmu.edu/datafly/index.html k-anonymity: a model for protecting privacy. Sweeney, L. International Jo ...
Definitions of Data Mining
... The discovery of new information in terms of patterns or rules from vast amounts of data is called Data Mining. It employs one or more computer learning techniques to automatically analyze and extract knowledge from data on order to find interesting structure in data. Data mining is actually one s ...
... The discovery of new information in terms of patterns or rules from vast amounts of data is called Data Mining. It employs one or more computer learning techniques to automatically analyze and extract knowledge from data on order to find interesting structure in data. Data mining is actually one s ...
Mathematical Programming in Support Vector Machines
... Where K is a nonlinear kernel, e.g.: Gaussian (Radial Basis) Kernel : ...
... Where K is a nonlinear kernel, e.g.: Gaussian (Radial Basis) Kernel : ...
Chapter 1 Business Driven Technology
... • Predict trends and behaviors • Identify unknown patterns • Determine how things fit together – What goes with what? ...
... • Predict trends and behaviors • Identify unknown patterns • Determine how things fit together – What goes with what? ...
Information Systems: A Manager*s Guide to Harnessing Technology
... • Data: Raw facts and figures • Information: Data presented in a context so that it can answer a question or support decision making • Knowledge: Insight derived from experience and expertise ...
... • Data: Raw facts and figures • Information: Data presented in a context so that it can answer a question or support decision making • Knowledge: Insight derived from experience and expertise ...
datamining-lect8
... • In a movie-rating system there are just a few types of users. • What we observe is an incomplete and noisy version of the true data • The rank-k approximation reconstructs the “true” matrix and we can provide ratings for movies that are not rated. ...
... • In a movie-rating system there are just a few types of users. • What we observe is an incomplete and noisy version of the true data • The rank-k approximation reconstructs the “true” matrix and we can provide ratings for movies that are not rated. ...
- VTUPlanet
... association between data, found neglected elements which might be very useful for trends and decision- making behavior. It has been described as “the nontrivial extraction of implicit, previously unknown, and potentially useful information from data” [5] and “the science of extracting useful informa ...
... association between data, found neglected elements which might be very useful for trends and decision- making behavior. It has been described as “the nontrivial extraction of implicit, previously unknown, and potentially useful information from data” [5] and “the science of extracting useful informa ...
1 - TerpConnect - University of Maryland
... Motivation -- continued Adjacency indicates a notion of similarity Given adjacency data w.r.t. n items or alternatives, can we display the items in a two-dimensional map? Traditional tools such as multidimensional scaling and Sammon maps work well with data in multidimensional format Can these tool ...
... Motivation -- continued Adjacency indicates a notion of similarity Given adjacency data w.r.t. n items or alternatives, can we display the items in a two-dimensional map? Traditional tools such as multidimensional scaling and Sammon maps work well with data in multidimensional format Can these tool ...
Document
... Example: large network analysis •The computers know only the edges, they can’t see the graph •Problem: find the real shape of the graph. ...
... Example: large network analysis •The computers know only the edges, they can’t see the graph •Problem: find the real shape of the graph. ...
syllabus
... (20%) Final Exam. Late Policy and Academic Honesty: The projects and homework assignments are due in class, on the specified due date. NO LATE SUBMISSIONS will be accepted. For fairness, this policy will be strictly enforced. Academic honesty is taken seriously. You must write up your own solutions ...
... (20%) Final Exam. Late Policy and Academic Honesty: The projects and homework assignments are due in class, on the specified due date. NO LATE SUBMISSIONS will be accepted. For fairness, this policy will be strictly enforced. Academic honesty is taken seriously. You must write up your own solutions ...
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.