CS 6220: Data Mining Techniques Course Information Grading
... • Work on the HW assignment as soon as it comes out – Time to ask questions and deal with unforeseen problems – We might not be able to answer all last-minute questions if there are too many right before the deadline ...
... • Work on the HW assignment as soon as it comes out – Time to ask questions and deal with unforeseen problems – We might not be able to answer all last-minute questions if there are too many right before the deadline ...
A Data Mining Course for Computer Science Primary Sources and
... Sometimes referred to as unsupervised learning Goal: find clusters of similar data Less accurate than supervised learning, but quite useful when no training set is available Where are the clusters below? How many are there? ...
... Sometimes referred to as unsupervised learning Goal: find clusters of similar data Less accurate than supervised learning, but quite useful when no training set is available Where are the clusters below? How many are there? ...
Data Visualization using IRIS Explorer
... Each region = 3 by 3 array of pixels 4 spectral bands per pixel ...
... Each region = 3 by 3 array of pixels 4 spectral bands per pixel ...
ITECH7406 - fdl Grades
... This course introduces students to business intelligence and data warehousing techniques used to analyse enterprise data sets. Topics may include theories and principles of data warehousing, business intelligence basics, value of DW and BI, relationship between DW and BI, DW architecture, DW types, ...
... This course introduces students to business intelligence and data warehousing techniques used to analyse enterprise data sets. Topics may include theories and principles of data warehousing, business intelligence basics, value of DW and BI, relationship between DW and BI, DW architecture, DW types, ...
Improving the Performance of K-Means Clustering For High
... technique; the central idea of PCA is to reduce the dimensionality of the data set consisting of a large number of variables[1]. This is achieved by transforming to a new set of variables (Principal Components) which are uncorrelated and, which are ordered so that the first few retain the most of th ...
... technique; the central idea of PCA is to reduce the dimensionality of the data set consisting of a large number of variables[1]. This is achieved by transforming to a new set of variables (Principal Components) which are uncorrelated and, which are ordered so that the first few retain the most of th ...
Introduction and Overview
... conversion of data into information dissemination of that information for the generation of human knowledge. ...
... conversion of data into information dissemination of that information for the generation of human knowledge. ...
Data Warehousing Concepts and Design
... Expanding the idea of dimensionality. A table with n attr. = a space with n dimensions. Managers usually ask multi-dimensional questions. Not easy in traditional DBs. Multi-dimensional relationships require multiple keys while traditional DBs have 1 key per record. OLAP useful with multi ...
... Expanding the idea of dimensionality. A table with n attr. = a space with n dimensions. Managers usually ask multi-dimensional questions. Not easy in traditional DBs. Multi-dimensional relationships require multiple keys while traditional DBs have 1 key per record. OLAP useful with multi ...
Anomaly Detection via Online Over-Sampling Principal Component
... Over-Sampling Principal Component Analysis ...
... Over-Sampling Principal Component Analysis ...
Database Systems: Design, Implementation, and Management
... ◦ If a small number of records have missing values, can omit them ◦ If many records are missing values on a small set of variables, can drop those variables (or use proxies) ◦ If many records have missing values, omission is not practical ...
... ◦ If a small number of records have missing values, can omit them ◦ If many records are missing values on a small set of variables, can drop those variables (or use proxies) ◦ If many records have missing values, omission is not practical ...
pillar pkmeans2 - NDSU Computer Science
... ABSTRACT: This paper describes an approach for the data mining technique called clustering using vertically structured data and k-means partitioning methodology. The partitioning methodology is based on the scalar product with judiciously chosen unit vectors. In any k-means clustering method, choosi ...
... ABSTRACT: This paper describes an approach for the data mining technique called clustering using vertically structured data and k-means partitioning methodology. The partitioning methodology is based on the scalar product with judiciously chosen unit vectors. In any k-means clustering method, choosi ...
Course Outline 2016 INFOSYS 722: Data Mining
... Exploring various data mining techniques and BI tools Exploring various BI Tools using case studies Exploring various Big Data tools in use Visualisation Emerging technologies: links with data analytics Presentations of Final Projects; handing in Research Essays ...
... Exploring various data mining techniques and BI tools Exploring various BI Tools using case studies Exploring various Big Data tools in use Visualisation Emerging technologies: links with data analytics Presentations of Final Projects; handing in Research Essays ...
Data Mining Methods - socialcomputing-iba
... According to the Economist, there’s a big market for such software. “By one estimate there are more than 100 programs for network analysis, also known as link analysis or predictive analysis. The raw data used may extend far beyond phone records to encompass information available from private an ...
... According to the Economist, there’s a big market for such software. “By one estimate there are more than 100 programs for network analysis, also known as link analysis or predictive analysis. The raw data used may extend far beyond phone records to encompass information available from private an ...
Data mining & Machine Learning Methods for Micro
... H0 - no difference between the treatment and the controlled samples H1 - treatment has an influence. Knowing the probability distribution of the T variable under H0 (Student law of p-1 ddl), the actual T is computed and compared to ...
... H0 - no difference between the treatment and the controlled samples H1 - treatment has an influence. Knowing the probability distribution of the T variable under H0 (Student law of p-1 ddl), the actual T is computed and compared to ...
Mining Complex Data Web Mining PageRank
... • Eigenvector equation, find dominant eigenvector; can be found by power method ...
... • Eigenvector equation, find dominant eigenvector; can be found by power method ...
Applications of Graph Based Pattern Recognition
... – Concatenate upper right diagonal of adjacency matrix into one long vector (d=4005) – Apply dissimilarity space embedding, using all graphs from the training set as prototypes (d=29) • Three standard classifiers were applied (all from WEKA): – SVM with linear kernel – Decision forest – Multilayer p ...
... – Concatenate upper right diagonal of adjacency matrix into one long vector (d=4005) – Apply dissimilarity space embedding, using all graphs from the training set as prototypes (d=29) • Three standard classifiers were applied (all from WEKA): – SVM with linear kernel – Decision forest – Multilayer p ...
Lars Arge - Department of Computer Science
... N I/Os take 256 x 103 sec = 4266 min = 71 hr N/B I/Os take 256/8 sec = 32 sec Lars Arge ...
... N I/Os take 256 x 103 sec = 4266 min = 71 hr N/B I/Os take 256/8 sec = 32 sec Lars Arge ...
Data Warehousing and Data Mining
... Cluster Analysis - Types of Data – Categorization of Major Clustering Methods - Kmeans – Partitioning Methods – Hierarchical Methods - Density-Based Methods –Grid Based Methods – Model-Based Clustering Methods – Clustering High Dimensional Data - Constraint – Based Cluster Analysis – Outlier Analysi ...
... Cluster Analysis - Types of Data – Categorization of Major Clustering Methods - Kmeans – Partitioning Methods – Hierarchical Methods - Density-Based Methods –Grid Based Methods – Model-Based Clustering Methods – Clustering High Dimensional Data - Constraint – Based Cluster Analysis – Outlier Analysi ...
Models Created by Data Mining
... • Puts too much power into the hands of Governmental Security Forces ...
... • Puts too much power into the hands of Governmental Security Forces ...
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.