Course : Data mining Topic : Locality
... recall : finding similar objects informal definition two problems 1. similarity search problem given a set X of objects (off-line) given a query object q (query time) find the object in X that is most similar to q 2. all-pairs similarity problem given a set X of objects (off-line) find all pairs of ...
... recall : finding similar objects informal definition two problems 1. similarity search problem given a set X of objects (off-line) given a query object q (query time) find the object in X that is most similar to q 2. all-pairs similarity problem given a set X of objects (off-line) find all pairs of ...
Chapter 6
... Uses MDLbased stopping criterion Employs postprocessing step to modify rules guided by MDL criterion ...
... Uses MDLbased stopping criterion Employs postprocessing step to modify rules guided by MDL criterion ...
Discovering Lag Intervals for Temporal Dependencies
... Figure 1, [5min, 6min] is the predicted time range, indicating when a database alert occurs after a disk capacity alert is received. Furthermore, the associated lag interval characterizes the cause of a temporal dependency. For example, if the database is writing a huge temporal log file which is lar ...
... Figure 1, [5min, 6min] is the predicted time range, indicating when a database alert occurs after a disk capacity alert is received. Furthermore, the associated lag interval characterizes the cause of a temporal dependency. For example, if the database is writing a huge temporal log file which is lar ...
Computing intersections in a set of line segments: the Bentley
... That is, each iteration takes O(log n) time. It follows that the total running time of the algorithm is O(n log n) + (2n + k) · O(log n) = O((n + k) log n). How much space does the algorithm use? The X- and Y -structures both have size O(n). Clearly, it takes O(k) space to store all k intersections. ...
... That is, each iteration takes O(log n) time. It follows that the total running time of the algorithm is O(n log n) + (2n + k) · O(log n) = O((n + k) log n). How much space does the algorithm use? The X- and Y -structures both have size O(n). Clearly, it takes O(k) space to store all k intersections. ...
Performance Analysis of Clustering using Partitioning and
... Text clustering is the method of combining text or documents which are similar and dissimilar to one another. In several text tasks, this text mining is used such as extraction of information and concept/entity, summarization of documents, modeling of relation with entity, categorization/classificat ...
... Text clustering is the method of combining text or documents which are similar and dissimilar to one another. In several text tasks, this text mining is used such as extraction of information and concept/entity, summarization of documents, modeling of relation with entity, categorization/classificat ...
PPT
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
Rough set methods in feature selection and recognition
... Skowron, 2000). Here, we introduce only the basic notation from rough set approach used in the paper. Suppose we are given two finite, non-empty sets U and A, where U is the universe of objects, cases, and A––a set of attributes, features. The pair IS ¼ ðU ; AÞ is called an information table. With ev ...
... Skowron, 2000). Here, we introduce only the basic notation from rough set approach used in the paper. Suppose we are given two finite, non-empty sets U and A, where U is the universe of objects, cases, and A––a set of attributes, features. The pair IS ¼ ðU ; AÞ is called an information table. With ev ...
Scalable Keyword Search on Large RDF Data
... version of the problem, the authors assumed edges across the boundaries of the partitions are weighted. A partition is treated as a supernode and edges crossing partitions are superedges. The supernodes and superedges form a new graph, which is considered as a summary the underlying graph data. By r ...
... version of the problem, the authors assumed edges across the boundaries of the partitions are weighted. A partition is treated as a supernode and edges crossing partitions are superedges. The supernodes and superedges form a new graph, which is considered as a summary the underlying graph data. By r ...
Spatial outlier detection based on iterative self
... In this paper, we propose an iterative self-organizing map (SOM) approach with robust distance estimation (ISOMRD) for spatial outlier detection. Generally speaking, spatial outliers are irregular data instances which have significantly distinct non-spatial attribute values compared to their spatial ...
... In this paper, we propose an iterative self-organizing map (SOM) approach with robust distance estimation (ISOMRD) for spatial outlier detection. Generally speaking, spatial outliers are irregular data instances which have significantly distinct non-spatial attribute values compared to their spatial ...
A Nonlinear Programming Algorithm for Solving Semidefinite
... Q2 What optimization method is best suited for (2)? In particular, can the optimization method exploit sparsity in the problem data? Q2 Since (2) is a nonconvex programming problem, can we even expect to find a global solution in practice? To answer Q1, we appeal to a theorem that posits the existen ...
... Q2 What optimization method is best suited for (2)? In particular, can the optimization method exploit sparsity in the problem data? Q2 Since (2) is a nonconvex programming problem, can we even expect to find a global solution in practice? To answer Q1, we appeal to a theorem that posits the existen ...
Classification - Computer Science and Engineering
... n The model is represented as classification rules, decision trees, or mathematical formulae Model usage: for classifying future or unknown objects n Estimate accuracy of the model n The known label of test sample is compared with the classified result from the model n Accuracy rate is the percentag ...
... n The model is represented as classification rules, decision trees, or mathematical formulae Model usage: for classifying future or unknown objects n Estimate accuracy of the model n The known label of test sample is compared with the classified result from the model n Accuracy rate is the percentag ...
Lecture Notes - Computer Science Department
... as having erroneous values for some of their attributes. The main problem is that their presence in our dataset can have an important impact on the results of some algorithms. A simple way of dealing with these data is to delete the examples. If the exceptional values appear only in a few of the att ...
... as having erroneous values for some of their attributes. The main problem is that their presence in our dataset can have an important impact on the results of some algorithms. A simple way of dealing with these data is to delete the examples. If the exceptional values appear only in a few of the att ...
. - Villanova Computer Science
... learning system can be viewed as learning a function which predicts the outcome from the inputs: – Given a training set of N example pairs (x1, y1) (x2,y2)...(xn,yn), where each yj was generated by an unknown function y = f(x), discover a function h that approximates the true function y ...
... learning system can be viewed as learning a function which predicts the outcome from the inputs: – Given a training set of N example pairs (x1, y1) (x2,y2)...(xn,yn), where each yj was generated by an unknown function y = f(x), discover a function h that approximates the true function y ...
Cluster analysis with ants Applied Soft Computing
... the final clustering by using during the classification different metrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired tech ...
... the final clustering by using during the classification different metrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired tech ...
EFFICIENCY OF LOCAL SEARCH WITH
... Definition 2.1. Attraction basin: The attraction basin of a local optimum mj is the set of points X1 , . . . , Xk of the search space such that a steepest ascent algorithm starting from Xi (1 ≤ i ≤ k) ends at the local optimum mj . The normalized size of the attraction basin of the local optimum mj ...
... Definition 2.1. Attraction basin: The attraction basin of a local optimum mj is the set of points X1 , . . . , Xk of the search space such that a steepest ascent algorithm starting from Xi (1 ≤ i ≤ k) ends at the local optimum mj . The normalized size of the attraction basin of the local optimum mj ...
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.