Our Proposed Approach - Intrusion Detection in Columbia University
... – Accuracy depends on selecting the right set of features. – For example, using only “# of failed logins” to detect “guessing passwd” may not be adequate: • guess_passwd :- #_failed_login >= 4. • this rule has high FP rate since a legitimate user can have typos when entering passwd. • need additiona ...
... – Accuracy depends on selecting the right set of features. – For example, using only “# of failed logins” to detect “guessing passwd” may not be adequate: • guess_passwd :- #_failed_login >= 4. • this rule has high FP rate since a legitimate user can have typos when entering passwd. • need additiona ...
Title Distributed Clustering Algorithm for Spatial Data Mining Author(s)
... model accuracy will be very poor [J.F.Laloux-11]. Both partitioning and hierarchical categories suffer from some drawbacks. For the partitioning class, we have k-means algorithm witch needs the number of clusters fixed in advance, while in the majority of cases K is not known, furthermore hierarchic ...
... model accuracy will be very poor [J.F.Laloux-11]. Both partitioning and hierarchical categories suffer from some drawbacks. For the partitioning class, we have k-means algorithm witch needs the number of clusters fixed in advance, while in the majority of cases K is not known, furthermore hierarchic ...
Title Distributed Clustering Algorithm for Spatial Data Mining Author(s)
... model accuracy will be very poor [J.F.Laloux-11]. Both partitioning and hierarchical categories suffer from some drawbacks. For the partitioning class, we have k-means algorithm witch needs the number of clusters fixed in advance, while in the majority of cases K is not known, furthermore hierarchic ...
... model accuracy will be very poor [J.F.Laloux-11]. Both partitioning and hierarchical categories suffer from some drawbacks. For the partitioning class, we have k-means algorithm witch needs the number of clusters fixed in advance, while in the majority of cases K is not known, furthermore hierarchic ...
The Generic Inverse Variance method
... ‘Logarithm’. Due to the fact that the standard error of the relative risk is on the natural log scale we need to highlight ‘Logarithm’ (for more information on logarithms see Bland and Altman). ...
... ‘Logarithm’. Due to the fact that the standard error of the relative risk is on the natural log scale we need to highlight ‘Logarithm’ (for more information on logarithms see Bland and Altman). ...
Volumetric MRI Classification for Alzheimer`s Diseases Based on
... immature estimation neglects the information of distances between local features which would be useful to eliminate noises from inter-subject anatomical variability. Moreover, its estimator has jumps at the edge and zero derivatives everywhere else13. As a result, noises which may be significant at ...
... immature estimation neglects the information of distances between local features which would be useful to eliminate noises from inter-subject anatomical variability. Moreover, its estimator has jumps at the edge and zero derivatives everywhere else13. As a result, noises which may be significant at ...
7class
... Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting ...
... Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting ...
Locally adaptive metrics for clustering high dimensional data
... Given a set of multivariate data, (partitional) clustering finds a partition of the points into clusters such that the points within a cluster are more similar to each other than to points in different clusters. The popular K-means or K-medoids methods compute one representative point per cluster, a ...
... Given a set of multivariate data, (partitional) clustering finds a partition of the points into clusters such that the points within a cluster are more similar to each other than to points in different clusters. The popular K-means or K-medoids methods compute one representative point per cluster, a ...
DBSCAN
... insert into result select regionQuery(sx, sy, Eps); insert into resultsize select count(*) from result; insert into seeds select rx, ry from result where (select size from resultsize)>=Minpts and (select ClId from SetofPoints where x=rx and y=ry)=-1; update SetofPoints set ClId=ClusterId where SQLCO ...
... insert into result select regionQuery(sx, sy, Eps); insert into resultsize select count(*) from result; insert into seeds select rx, ry from result where (select size from resultsize)>=Minpts and (select ClId from SetofPoints where x=rx and y=ry)=-1; update SetofPoints set ClId=ClusterId where SQLCO ...
A Comparative Analysis of Density Based Clustering
... DBSCAN's definition of a cluster is based on the notion of density reach ability. Basically, a point q is directly density-reachable from a point p if it is not farther away than a given distance є (i.e., is part of its є -neighborhood) and if p is surrounded by sufficiently many points such that on ...
... DBSCAN's definition of a cluster is based on the notion of density reach ability. Basically, a point q is directly density-reachable from a point p if it is not farther away than a given distance є (i.e., is part of its є -neighborhood) and if p is surrounded by sufficiently many points such that on ...
Discrimination-Aware Classifiers for Student Performance Prediction
... and DCI. To classify new instances, DAAR uses majority voting based on the number of rules that predict the same class. If the vote is tied, the DCI sum for all rules for each class is compared and the class with lower sum (i.e. less discrimination) is selected. ...
... and DCI. To classify new instances, DAAR uses majority voting based on the number of rules that predict the same class. If the vote is tied, the DCI sum for all rules for each class is compared and the class with lower sum (i.e. less discrimination) is selected. ...
FP-Outlier: Frequent Pattern Based Outlier Detection
... of dimensions. The method proposed by Aggarwal and Yu [4] considers data points in a local region of abnormally low density as outliers to conquer the curse of dimensionality. The main problem of their approach is that the outlier factor of each data object is determined only by the projection with ...
... of dimensions. The method proposed by Aggarwal and Yu [4] considers data points in a local region of abnormally low density as outliers to conquer the curse of dimensionality. The main problem of their approach is that the outlier factor of each data object is determined only by the projection with ...
Automating Operational Business Decisions Using Artificial
... and the complex relations between them. This makes the task time consuming and complex for humans to carry out accurately. Algorithms could support or even take over this task by learning to make certain decisions automatically as part of an Artificial Intelligence (AI) system. The research area of ...
... and the complex relations between them. This makes the task time consuming and complex for humans to carry out accurately. Algorithms could support or even take over this task by learning to make certain decisions automatically as part of an Artificial Intelligence (AI) system. The research area of ...
Title Data Preprocessing for Improving Cluster Analysis
... Figure 3.5 Visualizations of 2D datasets after preprocessed by CLUES. The results of D-IMPACT are shown in Figure 3.4. From these results, we can see that IMPACT algorithm can separate clusters, remove noise while retain the global structure of clusters. In contrast, CLUES incorrectly merge and frac ...
... Figure 3.5 Visualizations of 2D datasets after preprocessed by CLUES. The results of D-IMPACT are shown in Figure 3.4. From these results, we can see that IMPACT algorithm can separate clusters, remove noise while retain the global structure of clusters. In contrast, CLUES incorrectly merge and frac ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.