Data Reduction via Instance Selection
... Each data point has a weight and the sum of the weights is equal to the number of instances in the original data set. Obtaining squashed data • Model free, model dependent (or likelihood based) ...
... Each data point has a weight and the sum of the weights is equal to the number of instances in the original data set. Obtaining squashed data • Model free, model dependent (or likelihood based) ...
Mining Interesting Locations and Travel Sequences from GPS
... determine locations (stay points) and travel paths Combine users location histories and create TreeBased Hierarchical Graph Apply Hypertext Induced Topic Search to TBHG to infer location interest and user experience Create travel recomendations based on the data ...
... determine locations (stay points) and travel paths Combine users location histories and create TreeBased Hierarchical Graph Apply Hypertext Induced Topic Search to TBHG to infer location interest and user experience Create travel recomendations based on the data ...
Data Mining
... In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. ...
... In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. ...
Data mining and knowledge discovery in databases have been attr
... Knowledge Discovery in Databases (KDD) combines Data Warehousing/Databases and techniques from data mining, machine learning, pattern recognition, statistics… to automatically extract concepts and their interrelations and patterns of interest from large databases. Data Mining and Knowledge Discovery ...
... Knowledge Discovery in Databases (KDD) combines Data Warehousing/Databases and techniques from data mining, machine learning, pattern recognition, statistics… to automatically extract concepts and their interrelations and patterns of interest from large databases. Data Mining and Knowledge Discovery ...
Project 2: Classification
... Conduct classification on a data set. Run the J48 algorithm (decision tree) on the data. How does the tree look like? What is its classification accuracy? Run IB1 (nearest neighbor) on the data. Does it have a classifier? What is its classification accuracy? ...
... Conduct classification on a data set. Run the J48 algorithm (decision tree) on the data. How does the tree look like? What is its classification accuracy? Run IB1 (nearest neighbor) on the data. Does it have a classifier? What is its classification accuracy? ...
Referências para as disciplinas de Modelagem
... [3] Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd edition, 2009. [4] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. ...
... [3] Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd edition, 2009. [4] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. ...
STAT 6289-13
... Cluster analysis: Introduction, Similarity and distance, Characteristics of clustering algorithms, Center based clustering techniques, Hierarchical clustering, Density based clustering, Other clustering techniques, Scalable clustering algorithms, Cluster evaluation Visualization: Introduction, Gener ...
... Cluster analysis: Introduction, Similarity and distance, Characteristics of clustering algorithms, Center based clustering techniques, Hierarchical clustering, Density based clustering, Other clustering techniques, Scalable clustering algorithms, Cluster evaluation Visualization: Introduction, Gener ...
Interactive Data Exploration and Analytics
... users to interactively explore their data, receiving near-instant updates to every requested refinement. The focus is on interactivity and effective integration of techniques from data mining, visualization, and human-computer interaction. Topics of Interest include, but are not limited to - Interac ...
... users to interactively explore their data, receiving near-instant updates to every requested refinement. The focus is on interactivity and effective integration of techniques from data mining, visualization, and human-computer interaction. Topics of Interest include, but are not limited to - Interac ...
Data Mining and Knowledge Discovery in
... Data preparation tasks are likely to be performed multiple times, and not in any prescribed order Tasks include table, record, and attribute selection, as well as transformation and cleaning of data for modeling tools ...
... Data preparation tasks are likely to be performed multiple times, and not in any prescribed order Tasks include table, record, and attribute selection, as well as transformation and cleaning of data for modeling tools ...
Introduction to Data Mining
... 3 credit hours; elective for CS & CPE; 150 min. lecture each week Current Catalog Description This course will provide an introductory look at concepts and techniques in the field of data mining. After covering the introduction and terminologies to Data Mining, the techniques used to explore the lar ...
... 3 credit hours; elective for CS & CPE; 150 min. lecture each week Current Catalog Description This course will provide an introductory look at concepts and techniques in the field of data mining. After covering the introduction and terminologies to Data Mining, the techniques used to explore the lar ...
MENA
... Risk networks can be created for the identification of suspicious behavior patterns for the creation of self-adaptive counterintelligence systems. Profiles are created from multiple data sources in a totally anonymous manner without the need to centralize or move any data. These techniques improve d ...
... Risk networks can be created for the identification of suspicious behavior patterns for the creation of self-adaptive counterintelligence systems. Profiles are created from multiple data sources in a totally anonymous manner without the need to centralize or move any data. These techniques improve d ...
Course curriculum for STAN45 Data Mining and Visualization
... programming tools and applications in this field. By introducing principal ideas in statistical learning, the course helps students to understand methods in data mining and computational aspects of algorithm implementation. To make an algorithm efficient for handling very large scale data sets, issu ...
... programming tools and applications in this field. By introducing principal ideas in statistical learning, the course helps students to understand methods in data mining and computational aspects of algorithm implementation. To make an algorithm efficient for handling very large scale data sets, issu ...
BIS 541
... power of each of the input variables. 3. Consider a real life situation that interest you: an university registration system, an hospital information system or any other business problem. Answer the following in at most two page. a) Describe the environment with about 50-60 workds. b) Describe very ...
... power of each of the input variables. 3. Consider a real life situation that interest you: an university registration system, an hospital information system or any other business problem. Answer the following in at most two page. a) Describe the environment with about 50-60 workds. b) Describe very ...
Exploratory Analysis for Efficient Data Mining
... recognition of patterns for decision support. SAS Institute (SI) advocates a framework (SEMMA) for exploration and mining which systematizes the activities of sampling, exploring, modifying, modeling and assessing as an iterative stepwise process. Data exploration and mining can be accomplished with ...
... recognition of patterns for decision support. SAS Institute (SI) advocates a framework (SEMMA) for exploration and mining which systematizes the activities of sampling, exploring, modifying, modeling and assessing as an iterative stepwise process. Data exploration and mining can be accomplished with ...
Workshop on Applied Data Science
... Data Literacy is fast becoming the new baseline expectation that organizations have of new hires. This course will give you a solid foundation in the key elements of applied data science. Co-taught by two Carnegie Mellon professors who specialize in data analysis and data visualization, the course d ...
... Data Literacy is fast becoming the new baseline expectation that organizations have of new hires. This course will give you a solid foundation in the key elements of applied data science. Co-taught by two Carnegie Mellon professors who specialize in data analysis and data visualization, the course d ...
Classification Under the Relevant Set Correlation Model
... One of the most well-known classification methods in machine learning is that of knearest-neighbor (k-NN) classification, a voting strategy in which each object is assigned to the class most common among its k closest neighbors within a training set of examples. Despite its simplicity and effectiven ...
... One of the most well-known classification methods in machine learning is that of knearest-neighbor (k-NN) classification, a voting strategy in which each object is assigned to the class most common among its k closest neighbors within a training set of examples. Despite its simplicity and effectiven ...
New Scientific Data for Nowcasting and Forecasting Space Weather?
... • Samples times over ~15 years of geomagnetic and solar wind data • Storms rare but important • Balance dataset otherwise storms look like noise • Features selected like ...
... • Samples times over ~15 years of geomagnetic and solar wind data • Storms rare but important • Balance dataset otherwise storms look like noise • Features selected like ...
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.