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Cluster Analysis
... Integration of hierarchical with distance-based clustering BIRCH (1996): uses CF-tree and incrementally adjusts the quality of sub-clusters CURE (1998): selects well-scattered points from the cluster and then shrinks them towards the center of the cluster by a specified fraction CHAMELEON (199 ...
... Integration of hierarchical with distance-based clustering BIRCH (1996): uses CF-tree and incrementally adjusts the quality of sub-clusters CURE (1998): selects well-scattered points from the cluster and then shrinks them towards the center of the cluster by a specified fraction CHAMELEON (199 ...
Semantic Trajectories Modeling and Analysis
... Real-life trajectory data is far from being reliable enough for applications: ...
... Real-life trajectory data is far from being reliable enough for applications: ...
Intro_ lo
... and complexity of realworld problems. Doing so involves at least three major development methodologies: The first is hybridization, which optimally utilizes the advantages of existing methods (such as logic, including nonclassical logic, artificial neural networks, probabilistic and statistical reas ...
... and complexity of realworld problems. Doing so involves at least three major development methodologies: The first is hybridization, which optimally utilizes the advantages of existing methods (such as logic, including nonclassical logic, artificial neural networks, probabilistic and statistical reas ...
No Slide Title
... Interestingness measures and thresholds can be specified by the user with the statement: with threshold =
threshold_value
...
... Interestingness measures and thresholds can be specified by the user with the statement: with
mining clickstream-based data cubes
... provide multidimensional analysis over decisionsupport oriented data. To achieve this goal, these tools employ multidimensional models for the storage and presentation of data. In this systems, data is organized in cubes (or hypercubes), which are defined over a multidimensional space involving seve ...
... provide multidimensional analysis over decisionsupport oriented data. To achieve this goal, these tools employ multidimensional models for the storage and presentation of data. In this systems, data is organized in cubes (or hypercubes), which are defined over a multidimensional space involving seve ...
Operations research and data mining
... The benefit of using the dual is that the constraints are much simpler and easier to handle and that the training data only enters in (5) through the dot product ai Æ aj. This latter point is important for extending the approach to non-linear model. Requiring a hyperplane or a linear discrimination o ...
... The benefit of using the dual is that the constraints are much simpler and easier to handle and that the training data only enters in (5) through the dot product ai Æ aj. This latter point is important for extending the approach to non-linear model. Requiring a hyperplane or a linear discrimination o ...
Error Log Analytics using Big Data and MapReduce
... Log files provide valuable information about the functioning and performance of applications and devices. These files are used by the developer to monitor, debug, and troubleshoot the errors that may have occurred in the application. Manual processing of log data requires a huge amount of time, and ...
... Log files provide valuable information about the functioning and performance of applications and devices. These files are used by the developer to monitor, debug, and troubleshoot the errors that may have occurred in the application. Manual processing of log data requires a huge amount of time, and ...
Scalable Hierarchical Clustering Method for Sequences of
... Let us illustrate the approach with the following example (Fig. 1). We are given a web log file, which records paths used by users for navigation (e.g. the user s1 has visited the URLs: A, B, C, Z, and then D). Assume we are interested in discovering groups of users (sequences), whose behavior is si ...
... Let us illustrate the approach with the following example (Fig. 1). We are given a web log file, which records paths used by users for navigation (e.g. the user s1 has visited the URLs: A, B, C, Z, and then D). Assume we are interested in discovering groups of users (sequences), whose behavior is si ...
content enhancement
... Potential Impact on Access to Geoscience Literature Long-standing challenges of series and analytics (or lack thereof). Balance value of enhancement against increase in direct access to digital version thanks to surge of digitization projects for government publications. ...
... Potential Impact on Access to Geoscience Literature Long-standing challenges of series and analytics (or lack thereof). Balance value of enhancement against increase in direct access to digital version thanks to surge of digitization projects for government publications. ...
No Slide Title
... A. Silberschatz and A. Tuzhilin. What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge and Data Engineering, 8:970-974, Dec. 1996. S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database systems: Alternatives and implica ...
... A. Silberschatz and A. Tuzhilin. What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge and Data Engineering, 8:970-974, Dec. 1996. S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database systems: Alternatives and implica ...
Differentially Private Data Release for Data Mining
... discussion about the partition-based approach can be found in a survey paper [12]. Differential privacy has received considerable attention recently as a substitute for partition-based privacy models for PPDP. However, most of the research on differential privacy so far concentrates on the interactive ...
... discussion about the partition-based approach can be found in a survey paper [12]. Differential privacy has received considerable attention recently as a substitute for partition-based privacy models for PPDP. However, most of the research on differential privacy so far concentrates on the interactive ...
Introduction to Database Systems
... Specifies that patterns for data classification are to be mined Analyze clause specifies that classification is performed according to the values of (classifying_attribute_or_dimension) For categorical attributes or dimensions, each value represents a class (such as low-risk, medium risk, high risk) ...
... Specifies that patterns for data classification are to be mined Analyze clause specifies that classification is performed according to the values of (classifying_attribute_or_dimension) For categorical attributes or dimensions, each value represents a class (such as low-risk, medium risk, high risk) ...
Knowledge Discovery in Databases II Lecture 5: Stream
... – On avg, the bootstrap sample contains approximately 63% of the original D ...
... – On avg, the bootstrap sample contains approximately 63% of the original D ...
Movement Patterns in Spatio-Temporal Data
... is the number, or ratio, of entities that follow the rule. The spatial support takes the size of the involved regions into consideration. That is, a rule with support s involving a small region will have a larger spatial support than a rule with support s involving a larger region. Finally, the conf ...
... is the number, or ratio, of entities that follow the rule. The spatial support takes the size of the involved regions into consideration. That is, a rule with support s involving a small region will have a larger spatial support than a rule with support s involving a larger region. Finally, the conf ...
Nonlinear dimensionality reduction
![](https://commons.wikimedia.org/wiki/Special:FilePath/Lle_hlle_swissroll.png?width=300)
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.