Adaptive Learning and Mining for Data Streams and Frequent Patterns
... data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algo ...
... data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algo ...
The Algorithms of Updating Sequential Patterns
... sequence set. By adjusting the value of Min_nbd_supp, we can prune those negative border sequences whose values of support are small and that they have little effect on the computing results. Thus we can save the memory of the negative border sequences consuming. We also extend both the prefix and t ...
... sequence set. By adjusting the value of Min_nbd_supp, we can prune those negative border sequences whose values of support are small and that they have little effect on the computing results. Thus we can save the memory of the negative border sequences consuming. We also extend both the prefix and t ...
No - DidaWiki
... {} => class (initial rule) u R1: {A} => class (rule after adding conjunct) u Gain(R0, R1) = t [ log (p1/(p1+n1)) – log (p0/(p0 + n0)) ] u where t: number of positive instances covered by both R0 and R1 p0: number of positive instances covered by R0 n0: number of negative instances covered by R ...
... {} => class (initial rule) u R1: {A} => class (rule after adding conjunct) u Gain(R0, R1) = t [ log (p1/(p1+n1)) – log (p0/(p0 + n0)) ] u where t: number of positive instances covered by both R0 and R1 p0: number of positive instances covered by R0 n0: number of negative instances covered by R ...
AppSecEU08-SRF_Simon_Roses
... Security Visualization Becoming a popular field Needs a lot of research ...
... Security Visualization Becoming a popular field Needs a lot of research ...
Ontology-based knowledge discovery from unstructured and semi
... In the third setting, we extract business rules from process model repositories, where process models are encoded as text artefact. Business rules encode important business constraints (including legislative and regulatory compliance constraints) as well as organizational policies. Organizations are ...
... In the third setting, we extract business rules from process model repositories, where process models are encoded as text artefact. Business rules encode important business constraints (including legislative and regulatory compliance constraints) as well as organizational policies. Organizations are ...
Applying Data Mining Techniques Using SAS Enterprise Miner™
... After completing this course, you should be able to • identify business problems and determine suitable analytical methods • understand the difficulties presented by massive, opportunistic data • prepare data for analysis, including partitioning data and imputing missing values • train, assess, and ...
... After completing this course, you should be able to • identify business problems and determine suitable analytical methods • understand the difficulties presented by massive, opportunistic data • prepare data for analysis, including partitioning data and imputing missing values • train, assess, and ...
Semantic Trajectories Modeling and Analysis
... spatio-temporal characteristics of trajectories (e.g., which percentage of trajectories show an average speed over 10km/h?). However, most application analyses require complementing raw data with additional information from the application context. For example, interpreting trajectories of persons w ...
... spatio-temporal characteristics of trajectories (e.g., which percentage of trajectories show an average speed over 10km/h?). However, most application analyses require complementing raw data with additional information from the application context. For example, interpreting trajectories of persons w ...
Oracle Database 11g: Data Mining Techniques
... print this document solely for your own use in an Oracle training course. The document may not be modified or altered in any way. Except where your use constitutes "fair use" under copyright law, you may not use, share, download, upload, copy, print, display, perform, reproduce, publish, license, po ...
... print this document solely for your own use in an Oracle training course. The document may not be modified or altered in any way. Except where your use constitutes "fair use" under copyright law, you may not use, share, download, upload, copy, print, display, perform, reproduce, publish, license, po ...
Cluster Analysis for Large, High
... FastMap algorithm (Faloutsos & Lin, 1995), which maps objects to a low-dimensional space in an almost distance-preserving manner, has proven rather successful (e.g. Ganti et al., 1999). On the other hand, the higher the number of attributes, the more likely it is to have an increased number of irrel ...
... FastMap algorithm (Faloutsos & Lin, 1995), which maps objects to a low-dimensional space in an almost distance-preserving manner, has proven rather successful (e.g. Ganti et al., 1999). On the other hand, the higher the number of attributes, the more likely it is to have an increased number of irrel ...
PPT - Personal Web Pages
... What we’re doing in effect: solving the Knearest neighbor problem for a query vector In general, we do not know how to do this efficiently for high-dimensional spaces But it is solvable for short queries, and standard indexes support this well ...
... What we’re doing in effect: solving the Knearest neighbor problem for a query vector In general, we do not know how to do this efficiently for high-dimensional spaces But it is solvable for short queries, and standard indexes support this well ...
Spatiotemporal Data Mining: A Computational Perspective
... The interdisciplinary nature of spatiotemporal data mining means that its techniques must developed with awareness of the underlying physics or theories in the application domains [21]. For example, climate science studies find that observable predictors for climate phenomena discovered by data scie ...
... The interdisciplinary nature of spatiotemporal data mining means that its techniques must developed with awareness of the underlying physics or theories in the application domains [21]. For example, climate science studies find that observable predictors for climate phenomena discovered by data scie ...
No Slide Title
... reduce irrelevant information—infrequent items are gone frequency descending ordering: more frequent items are more likely to be shared never be larger than the original database (if not count node-links and counts) Example: For Connect-4 DB, compression ratio could be over 100 ...
... reduce irrelevant information—infrequent items are gone frequency descending ordering: more frequent items are more likely to be shared never be larger than the original database (if not count node-links and counts) Example: For Connect-4 DB, compression ratio could be over 100 ...
Exploratory Visualization of Inter-Organizational Networks
... In the past two decades, two trends related to data have risen that are shaping the way institutions and individuals operate. On one hand, data has exponentially increased in volume, both in globally and in terms of data produced and consumed by single organizations as a part of their normal operati ...
... In the past two decades, two trends related to data have risen that are shaping the way institutions and individuals operate. On one hand, data has exponentially increased in volume, both in globally and in terms of data produced and consumed by single organizations as a part of their normal operati ...
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.