Data Warehouse as a Backbone for Business Intelligence: Issues
... categorized into concepts that are suggested by the data rather than imposed from outside is mandatory (Agar, 1980; Al-Debei and Avison, 2010). To be aligned with the aforementioned principles, we analyzed the collected data thematically. In fact, a bottom-up open coding procedure was followed here ...
... categorized into concepts that are suggested by the data rather than imposed from outside is mandatory (Agar, 1980; Al-Debei and Avison, 2010). To be aligned with the aforementioned principles, we analyzed the collected data thematically. In fact, a bottom-up open coding procedure was followed here ...
Data mining, data fusion, and libraries - Purdue e-Pubs
... the road. From this anonymous data, TomTom knows exactly where, in which direction and at what speed all these mobile phone users are travelling throughout the road network. This real‐time data is combined with other existing quality traffic information sources, resulting in the most complete and ...
... the road. From this anonymous data, TomTom knows exactly where, in which direction and at what speed all these mobile phone users are travelling throughout the road network. This real‐time data is combined with other existing quality traffic information sources, resulting in the most complete and ...
GeoDMA – Geographic Data Mining Analyst
... ecologically meaningful units. Such land units can be used as the basis for analysis and assessment [31]. Another concern is how to build a semantic network for the interpretation task. User experience shows that there are no simple rules for building such networks, and this task may require conside ...
... ecologically meaningful units. Such land units can be used as the basis for analysis and assessment [31]. Another concern is how to build a semantic network for the interpretation task. User experience shows that there are no simple rules for building such networks, and this task may require conside ...
Clinical Adverse Events Data Analysis and Visualization
... Semantic zooming is a distortion technique that displays the object in a fisheye view. By moving around and changing the scale, the user can customize the data display; and 5) produce slide show of static images The SAS System implements several new technologies such as Java applets and ActiveX Cont ...
... Semantic zooming is a distortion technique that displays the object in a fisheye view. By moving around and changing the scale, the user can customize the data display; and 5) produce slide show of static images The SAS System implements several new technologies such as Java applets and ActiveX Cont ...
P-N-RMiner: A Generic Framework for Mining Interesting Structured
... and whether to take it uniform throughout the day. In fact, the optimal discretisation could vary for various lifestyle patterns. An alternative approach could be to take the mean and possibly higher-order statistics of the check-in times for each user, and find patterns in this summary description. ...
... and whether to take it uniform throughout the day. In fact, the optimal discretisation could vary for various lifestyle patterns. An alternative approach could be to take the mean and possibly higher-order statistics of the check-in times for each user, and find patterns in this summary description. ...
Chapter 3: Supervised Learning
... kNN can deal with complex and arbitrary decision boundaries. Despite its simplicity, researchers have shown that the classification accuracy of kNN can be quite strong and in many cases as accurate as those elaborated methods. kNN is slow at the classification time kNN does not produce an understand ...
... kNN can deal with complex and arbitrary decision boundaries. Despite its simplicity, researchers have shown that the classification accuracy of kNN can be quite strong and in many cases as accurate as those elaborated methods. kNN is slow at the classification time kNN does not produce an understand ...
Using Data Mining Techniques for Improving Building Life Cycle
... The construction industry has adapted the information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. Hence, the data generated within the construction industry has become increasingly overwhelming. The growth of many business, ...
... The construction industry has adapted the information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. Hence, the data generated within the construction industry has become increasingly overwhelming. The growth of many business, ...
CS6220: DATA MINING TECHNIQUES
... • Decision trees, naïve Bayesian classification, support vector ...
... • Decision trees, naïve Bayesian classification, support vector ...
Lecture 5 - Wiki Index
... models can be evaluated according to current standard measures used in the health sciences. To this end, tools have been developed that make this possible, and some novel medical applications have been devised in which the tools are put to use. Rough set theory provides a framework in which discerni ...
... models can be evaluated according to current standard measures used in the health sciences. To this end, tools have been developed that make this possible, and some novel medical applications have been devised in which the tools are put to use. Rough set theory provides a framework in which discerni ...
Introduction What is Data Mining ? "
... A Data Mining Query Language for Relational Databases (Han et al, ...
... A Data Mining Query Language for Relational Databases (Han et al, ...
Preface - Society for Industrial and Applied Mathematics
... Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. There have been many clustering algorithms scattered in publications in very diversified areas such as pattern recognition, artificial intelligence, information technology, image processing, biology, p ...
... Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. There have been many clustering algorithms scattered in publications in very diversified areas such as pattern recognition, artificial intelligence, information technology, image processing, biology, p ...
Prediction of Churn Behavior of Bank Customers Using Data Mining
... customers, say 50 per cent of them have churned away in February. This means, the model will not be fully trained with the behavior of churn customers before churning as only one month’s activity is analyzed. This problem occurred because of fixing the timeline before hand as shown in figure 1. In t ...
... customers, say 50 per cent of them have churned away in February. This means, the model will not be fully trained with the behavior of churn customers before churning as only one month’s activity is analyzed. This problem occurred because of fixing the timeline before hand as shown in figure 1. In t ...
Modified Tree Classification in Data Mining
... Backpropagation is a neural network learning algorithm. The field of neural networks was originally kindled by psychologists and neurobiologists who sought to develop and test computational analogues of neurons. A neural network is a set of connected input/output units in which each unit has a weigh ...
... Backpropagation is a neural network learning algorithm. The field of neural networks was originally kindled by psychologists and neurobiologists who sought to develop and test computational analogues of neurons. A neural network is a set of connected input/output units in which each unit has a weigh ...
Introduction to Data Warehouses
... longer than that of operational systems – Operational database: current value data – Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) ...
... longer than that of operational systems – Operational database: current value data – Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) ...
Chapter 10: XML
... • The earliest OLAP systems used multidimensional arrays in memory to store data cubes, and are referred to as multidimensional OLAP (MOLAP) systems. • OLAP implementations using only relational database features are called relational OLAP (ROLAP) systems • Hybrid systems, which store some summaries ...
... • The earliest OLAP systems used multidimensional arrays in memory to store data cubes, and are referred to as multidimensional OLAP (MOLAP) systems. • OLAP implementations using only relational database features are called relational OLAP (ROLAP) systems • Hybrid systems, which store some summaries ...
A Survey and Analysis on Classification and Regression
... classification, artificial neural networks, support vector machines, decision trees, logistic regression, etc. have been used to develop models in healthcare research (Mythili T., 2014). Classification divides data samples into target classes. The classification technique predicts the target class f ...
... classification, artificial neural networks, support vector machines, decision trees, logistic regression, etc. have been used to develop models in healthcare research (Mythili T., 2014). Classification divides data samples into target classes. The classification technique predicts the target class f ...
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.