Data Mining Techniques Based on Grey System
... One of the main tasks facing the theories of Grey system is to seek the mathematic relations and movement rule among factors themselves and between factors, based on behavioral data of social, economic, et al [J.L.Deng,1985][S.F.Liu,1998]. In Grey system theories, it is through the organization of r ...
... One of the main tasks facing the theories of Grey system is to seek the mathematic relations and movement rule among factors themselves and between factors, based on behavioral data of social, economic, et al [J.L.Deng,1985][S.F.Liu,1998]. In Grey system theories, it is through the organization of r ...
A Data Mining Course for Computer Science: Primary Sources and
... Traditional algorithms such as decision trees, neural networks, and support vector machines are covered in my AI course, so they get only peripheral mention here. Of course, one could cover these topics in more detail if one wished. Instead, I focus on the k-Nearest Neighbor algorithm from a data mi ...
... Traditional algorithms such as decision trees, neural networks, and support vector machines are covered in my AI course, so they get only peripheral mention here. Of course, one could cover these topics in more detail if one wished. Instead, I focus on the k-Nearest Neighbor algorithm from a data mi ...
A single pass algorithm for clustering evolving data streams
... data. These algorithms apply a divide-and-conquer technique that partitions the data stream in disjoint pieces and clusters each piece by extending the k-Median algorithm. A theoretical study of the approximation error obtained in using the extended schema is also provided in Guha et al. (2003). The ...
... data. These algorithms apply a divide-and-conquer technique that partitions the data stream in disjoint pieces and clusters each piece by extending the k-Median algorithm. A theoretical study of the approximation error obtained in using the extended schema is also provided in Guha et al. (2003). The ...
Teaching Data Mining: the University of Alabama and SAS®
... feedback from these students enrolled this year has been extremely positive. In some cases, students from other departments have presented seminars to the Statistics faculty and graduate students. We have also created a research group that will focus on credit scoring and financial risk assessment f ...
... feedback from these students enrolled this year has been extremely positive. In some cases, students from other departments have presented seminars to the Statistics faculty and graduate students. We have also created a research group that will focus on credit scoring and financial risk assessment f ...
30. An Efficient Index Support for Item Set Mining using
... selectively loading from disk the index portion essential for the extraction process. Each node contains three pointers to nodes in the tree. Each pointer stores the physical location of the corresponding node. An arbitrary node (e.g., [p:3] in the example I-Tree in Fig. 2a) involves the following l ...
... selectively loading from disk the index portion essential for the extraction process. Each node contains three pointers to nodes in the tree. Each pointer stores the physical location of the corresponding node. An arbitrary node (e.g., [p:3] in the example I-Tree in Fig. 2a) involves the following l ...
Z04404159163
... type qualifiers that apply to functions and variables. CUDA has some specific functions, called kernels. A kernel can be a function or a full program invoked by the CPU. It is executed N number of times in parallel on GPU by using N number of threads. CUDA also provides shared memory and synchroniza ...
... type qualifiers that apply to functions and variables. CUDA has some specific functions, called kernels. A kernel can be a function or a full program invoked by the CPU. It is executed N number of times in parallel on GPU by using N number of threads. CUDA also provides shared memory and synchroniza ...
Classifier Technology and the Illusion of Progress
... This section demonstrates that the extra performance to be achieved by more sophisticated classification rules, beyond that attained by simple methods, is small. It follows that if aspects of the classification problem are not accurately described (e.g., if incorrect distributions have been used, in ...
... This section demonstrates that the extra performance to be achieved by more sophisticated classification rules, beyond that attained by simple methods, is small. It follows that if aspects of the classification problem are not accurately described (e.g., if incorrect distributions have been used, in ...
DataScience@irit2016 public.pptx
... • Exponential growth, generated by all kinds of programs, networks and devices • E.g. Web 2.0 (social networks, etc.), mobile devices, computer simulations, satellites, radiotelescopes, sensors, etc. ...
... • Exponential growth, generated by all kinds of programs, networks and devices • E.g. Web 2.0 (social networks, etc.), mobile devices, computer simulations, satellites, radiotelescopes, sensors, etc. ...
pptx
... large incoming flow from nodes with large quantity of liquid. Mathematically, we compute an eigenvector of a matrix defined by the adjacency matrix of the graph ...
... large incoming flow from nodes with large quantity of liquid. Mathematically, we compute an eigenvector of a matrix defined by the adjacency matrix of the graph ...
A Survey on Data Mining with Big data - Applications
... the findings by applying the detected patterns to new subsets of data. It uses sophisticated algorithms for the process of sorting through large amounts of data sets and picking out relevant information. Data mining tools predict future and behaviors, allowing businesses to make proactive, knowledge ...
... the findings by applying the detected patterns to new subsets of data. It uses sophisticated algorithms for the process of sorting through large amounts of data sets and picking out relevant information. Data mining tools predict future and behaviors, allowing businesses to make proactive, knowledge ...
Explanation-Oriented Association Mining Using a Combination of
... Association mining was first introduced using transaction databases and deals with purchasing patterns of customers [1]. A set of items are associated if they are bought together by many customers. Some authors extended the original associations to negative associations [20]. ...
... Association mining was first introduced using transaction databases and deals with purchasing patterns of customers [1]. A set of items are associated if they are bought together by many customers. Some authors extended the original associations to negative associations [20]. ...
Teradata Warehouse Miner How to Use Teradata Warehouse Miner
... Description—uses descriptive statistics to better understand and profile areas of interest. Thus a variety of well known statistical tools and methods are used for this task—including frequency charts and other graphical output, measures of central tendency and variation. Data Mining Tasks with a Ta ...
... Description—uses descriptive statistics to better understand and profile areas of interest. Thus a variety of well known statistical tools and methods are used for this task—including frequency charts and other graphical output, measures of central tendency and variation. Data Mining Tasks with a Ta ...
Data Mining Newsletter 2016
... The Data Mining (DM) Subdivisions of the Institute for Operations Research and Management Sciences (INFORMS) organized the Workshop on Data Mining and Analytics (INFORMS DM-Analytics 2015) on Oct 31, 2015 at Philadelphia, PA, in conjunction with the 2015 INFORMS Annual Conference. A total of 25 pape ...
... The Data Mining (DM) Subdivisions of the Institute for Operations Research and Management Sciences (INFORMS) organized the Workshop on Data Mining and Analytics (INFORMS DM-Analytics 2015) on Oct 31, 2015 at Philadelphia, PA, in conjunction with the 2015 INFORMS Annual Conference. A total of 25 pape ...
Density-based hierarchical clustering for streaming data
... two clusters, which may lead to unsatisfactory results. In fact, density plays an important role in clustering (Cao et al., 2006; Lu et al., 2008). OPTICS (Ankerst et al., 1999) is a classical agglomerative algorithm based on density where two factors (core-distance and reachability-distance) are us ...
... two clusters, which may lead to unsatisfactory results. In fact, density plays an important role in clustering (Cao et al., 2006; Lu et al., 2008). OPTICS (Ankerst et al., 1999) is a classical agglomerative algorithm based on density where two factors (core-distance and reachability-distance) are us ...
Applying Data Mining Techniques to a Health Insurance
... and relationships which represent appropriate utilization of services. To date, most data analysis has focused on the area of detection and prevention of fraud and inappropriate practice. Inappropriate practice deals with issues such as requesting or providing services which are unreasonable, unnec ...
... and relationships which represent appropriate utilization of services. To date, most data analysis has focused on the area of detection and prevention of fraud and inappropriate practice. Inappropriate practice deals with issues such as requesting or providing services which are unreasonable, unnec ...
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.