Chapter 12. Outlier Detection
... The index-based, nested-loop based, and grid-based approaches were explored [KN98, KNT00] to speed up distance-based outlier detection. Bay and Schwabacher [BS03] pointed out that the CPU runtime of the nested-loop method is often scalable with respect to the database size. Tao, Xiao, and Zhou [TXZ0 ...
... The index-based, nested-loop based, and grid-based approaches were explored [KN98, KNT00] to speed up distance-based outlier detection. Bay and Schwabacher [BS03] pointed out that the CPU runtime of the nested-loop method is often scalable with respect to the database size. Tao, Xiao, and Zhou [TXZ0 ...
Using SAS® Enterprise Miner - for Data Quality Monitoring in the Veterans Health Administration's External Peer Review Program
... When used strictly for predictive purposes the neural network seemed to exhibit the best performance of three fitted models. But all of the three fitted models performed well, with very low misclassification rates. An examination of the groupings made by SAS® EM™ in the "Variable Selection" step cou ...
... When used strictly for predictive purposes the neural network seemed to exhibit the best performance of three fitted models. But all of the three fitted models performed well, with very low misclassification rates. An examination of the groupings made by SAS® EM™ in the "Variable Selection" step cou ...
Efficient Relevance Feedback for Content
... • High-priced manual annotation cost is prohibitive in coping with a large-scale data set. • Inappropriate automated annotation yields the distorted results for semantic image retrieval. • Content-Based Image Retrieval(CBIR) is to present an image conceptually ,with a set of low-level visual feature ...
... • High-priced manual annotation cost is prohibitive in coping with a large-scale data set. • Inappropriate automated annotation yields the distorted results for semantic image retrieval. • Content-Based Image Retrieval(CBIR) is to present an image conceptually ,with a set of low-level visual feature ...
Mining Sequential Patterns of Event Streams in a Smart Home Application
... We are given a set S = {S 1 , S 2 , . . . , S |S| } of |S| different streams arriving from different observed parameters collected from the smart home. Each stream S k is represented by streaming, time-stamped interval-based events that evolve over the time. Thus, the first n items of stream S k are ...
... We are given a set S = {S 1 , S 2 , . . . , S |S| } of |S| different streams arriving from different observed parameters collected from the smart home. Each stream S k is represented by streaming, time-stamped interval-based events that evolve over the time. Thus, the first n items of stream S k are ...
Classification
... examples (cases) to classify unknown cases § Uses lazy evaluation and analysis of similar instances § Methodology § Instances represented by rich symbolic descriptions – similarity measures § Multiple retrieved cases may be combined § Tight coupling between case retrieval, knowledge-based re ...
... examples (cases) to classify unknown cases § Uses lazy evaluation and analysis of similar instances § Methodology § Instances represented by rich symbolic descriptions – similarity measures § Multiple retrieved cases may be combined § Tight coupling between case retrieval, knowledge-based re ...
An Overview of Data Warehousing and OLAP Technology
... sources: these might include external sources such as stock market feeds, in addition to several operational databases. The different sources might contain data of varying quality, or use inconsistent representations, codes and formats, which have to be reconciled. Finally, supporting the multidimen ...
... sources: these might include external sources such as stock market feeds, in addition to several operational databases. The different sources might contain data of varying quality, or use inconsistent representations, codes and formats, which have to be reconciled. Finally, supporting the multidimen ...
CONTINUOUS FREQUENT DATASET FOR MINING HIGH UTILITY
... leads to better understanding of the underlying processes. Data mining activities uses combination of techniques from database artificial intelligence, statistics, technologies machine learning. In General, the data mining (sometimes called data or knowledge discovery) is the process of analyzing d ...
... leads to better understanding of the underlying processes. Data mining activities uses combination of techniques from database artificial intelligence, statistics, technologies machine learning. In General, the data mining (sometimes called data or knowledge discovery) is the process of analyzing d ...
Turban: Chapter 5: Data Mining for Business Intelligence
... hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
An Overview of Data Warehousing and OLAP Technology
... sources: these might include external sources such as stock market feeds, in addition to several operational databases. The different sources might contain data of varying quality, or use inconsistent representations, codes and formats, which have to be reconciled. Finally, supporting the multidimen ...
... sources: these might include external sources such as stock market feeds, in addition to several operational databases. The different sources might contain data of varying quality, or use inconsistent representations, codes and formats, which have to be reconciled. Finally, supporting the multidimen ...
(SQL Multimedia and Application Packages \(SQL
... parts), rotate an image (such as changing its orientation from horizontal to vertical), and creating a “thumbnail” image (a lower resolution image used for quick display). Another group of data types are used to describe various features of images. The SI_AverageColor type is used to represent the “ ...
... parts), rotate an image (such as changing its orientation from horizontal to vertical), and creating a “thumbnail” image (a lower resolution image used for quick display). Another group of data types are used to describe various features of images. The SI_AverageColor type is used to represent the “ ...
Lecture3_GIS_principles - University of Western Cape
... Uses “pixels” for location and value attributes and includes satellite-images and digital aerial photos are already in this format. Each grid will have a value that corresponds to some feature, for example water might have a value of 6 and there fore all grids which have a value of 6 represents wate ...
... Uses “pixels” for location and value attributes and includes satellite-images and digital aerial photos are already in this format. Each grid will have a value that corresponds to some feature, for example water might have a value of 6 and there fore all grids which have a value of 6 represents wate ...
introduction
... that the individual would be able to know what they are looking for. Another issue of data mining is the rapid increase of information in the database. There is a risk wherein the data would be so large it would be really challenging to keep track of all the data entered in the database. The techno ...
... that the individual would be able to know what they are looking for. Another issue of data mining is the rapid increase of information in the database. There is a risk wherein the data would be so large it would be really challenging to keep track of all the data entered in the database. The techno ...
The Scientific Data Mining Process
... representing each object. In scientific data sets, the data may need to be processed before we can even identify the objects in the data. These processing steps may include tasks such as: • Data size reduction: One task that is very helpful in the initial processing is to reduce the size of the data ...
... representing each object. In scientific data sets, the data may need to be processed before we can even identify the objects in the data. These processing steps may include tasks such as: • Data size reduction: One task that is very helpful in the initial processing is to reduce the size of the data ...
Minimizing Cost when using Globally Distributed Cloud Services: A
... in several Gigabits per second. This makes the application data-intensive. Furthermore, networks are not restricted to a small room or a building and can spread throughout the globe. In such a distributed setting, it becomes critical to optimize the cost of data transfer from distributed sources in ...
... in several Gigabits per second. This makes the application data-intensive. Furthermore, networks are not restricted to a small room or a building and can spread throughout the globe. In such a distributed setting, it becomes critical to optimize the cost of data transfer from distributed sources in ...
Levelwise Search and Borders of Theories in Knowledge Discovery
... set of patterns L. The interpretation of such a rule is that in most cases where ϕ applies to the data, also θ applies. Examples of cases where this formulation applies are the discovery of association rules, episode rules, and integrity constraints in relational databases. The crucial observation i ...
... set of patterns L. The interpretation of such a rule is that in most cases where ϕ applies to the data, also θ applies. Examples of cases where this formulation applies are the discovery of association rules, episode rules, and integrity constraints in relational databases. The crucial observation i ...
CS690L: Clustering What`s Clustering Quality of Clustering
... • Insurance: Identifying groups of motor insurance policy holders with a high average claim cost • City-planning: Identifying groups of houses according to their house type, value, and geographical location • Earth-quake studies: Observed earth quake epicenters should be clustered along continent fa ...
... • Insurance: Identifying groups of motor insurance policy holders with a high average claim cost • City-planning: Identifying groups of houses according to their house type, value, and geographical location • Earth-quake studies: Observed earth quake epicenters should be clustered along continent fa ...
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.