Self-Tuning Clustering: An Adaptive Clustering Method for
... paper significantly outperforms previous efforts [5][7] in the execution efficiency and also the clustering quality for both synthetic and real market-basket data. This paper is organized as follows. Preliminaries are given in Section 2. In Section 3, algorithm STC is devised for clustering market-bask ...
... paper significantly outperforms previous efforts [5][7] in the execution efficiency and also the clustering quality for both synthetic and real market-basket data. This paper is organized as follows. Preliminaries are given in Section 2. In Section 3, algorithm STC is devised for clustering market-bask ...
Feature Selection
... unsupervised feature selection is described in Fig. 4b, which is very similar to supervised feature selection, except that there’s no label information involved in the feature selection phase and the model learning phase. Without label information to define feature relevance, unsupervised feature se ...
... unsupervised feature selection is described in Fig. 4b, which is very similar to supervised feature selection, except that there’s no label information involved in the feature selection phase and the model learning phase. Without label information to define feature relevance, unsupervised feature se ...
Calibration by correlation using metric embedding from non
... The scenario described in Problem 1 is sometimes called non-metric multidimensional scaling. The word “non-metric” is used because the metric information, contained in the distances d(si , sj ), is lost by the application of the unknown function f . In certain applications, it is not important for t ...
... The scenario described in Problem 1 is sometimes called non-metric multidimensional scaling. The word “non-metric” is used because the metric information, contained in the distances d(si , sj ), is lost by the application of the unknown function f . In certain applications, it is not important for t ...
Application of Data mining in Medical Applications
... Table 1 Accuracy for the WEKA software .......................................................................................... 50 Table 2 Example of Confusion matrix ................................................................................................. 50 Table 3 Confusion matrix of th ...
... Table 1 Accuracy for the WEKA software .......................................................................................... 50 Table 2 Example of Confusion matrix ................................................................................................. 50 Table 3 Confusion matrix of th ...
Mathematical Programming for Data Mining: Formulations and
... training set of cases of one class versus another and let the data mining system build a model for distinguishing one class from another. The system can then apply the extracted classifier to search the full database for events of interest. This is typically more feasible because examples are usuall ...
... training set of cases of one class versus another and let the data mining system build a model for distinguishing one class from another. The system can then apply the extracted classifier to search the full database for events of interest. This is typically more feasible because examples are usuall ...
Ensemble of Feature Selection Techniques for High
... symmetrical uncertainty, relief, random forests and linear support vector machines. The ensemble method used in this study is instance perturbation. The ensembles were evaluated using k-nearest neighbour, random forests and support vector machines. The experimental results have shown that robustnes ...
... symmetrical uncertainty, relief, random forests and linear support vector machines. The ensemble method used in this study is instance perturbation. The ensembles were evaluated using k-nearest neighbour, random forests and support vector machines. The experimental results have shown that robustnes ...
Clustering of Concept Drift Categorical Data Using Our
... Example 2: Consider the example shown in fig 2. The last clustering result C1 and current temporal clustering result C12 is compared with each other by the equation (3). Let us take the threshold OUTH is 0.4, the cluster variation threshold (ϵ) point is 0.3 and the cluster threshold difference is se ...
... Example 2: Consider the example shown in fig 2. The last clustering result C1 and current temporal clustering result C12 is compared with each other by the equation (3). Let us take the threshold OUTH is 0.4, the cluster variation threshold (ϵ) point is 0.3 and the cluster threshold difference is se ...
Mining Multitemporal in-situ Heterogeneous Monitoring
... elements in P . To that end, we define a weight vector W = [wp , wN , wE , wS , wW ] ...
... elements in P . To that end, we define a weight vector W = [wp , wN , wE , wS , wW ] ...
ROUGH SETS METHODS IN FEATURE REDUCTION AND
... where µ represents the total data mean and the determinant |Sb | denotes a scalar representation of the between-class scatter matrix, and similarly, the determinant |Sw | denotes a scalar representation of the within-class scatter matrix. Criteria based on minimum concept description. Based on the m ...
... where µ represents the total data mean and the determinant |Sb | denotes a scalar representation of the between-class scatter matrix, and similarly, the determinant |Sw | denotes a scalar representation of the within-class scatter matrix. Criteria based on minimum concept description. Based on the m ...
3. Answering Cube Queries Using Statistics Trees
... Arbor software's Essbase [3], Oracle Express [27] and Pilot LightShip [28] are based on MOLAP technology. The latest trend is to combine ROLAP and MOLAP in order to take advantage of the best of both worlds. For example, in PARSIMONY, some of the operations within sparse chunks are relational while ...
... Arbor software's Essbase [3], Oracle Express [27] and Pilot LightShip [28] are based on MOLAP technology. The latest trend is to combine ROLAP and MOLAP in order to take advantage of the best of both worlds. For example, in PARSIMONY, some of the operations within sparse chunks are relational while ...
An Overview of Data Mining Techniques
... This equation still describes a line but it is now a line in a6 dimensional space rather than the two dimensional space. By transforming the predictors by squaring, cubing or taking their square root it is possible to use the same general regression methodology and now create much more complex model ...
... This equation still describes a line but it is now a line in a6 dimensional space rather than the two dimensional space. By transforming the predictors by squaring, cubing or taking their square root it is possible to use the same general regression methodology and now create much more complex model ...
DATA MINING LAB MANUAL
... performed using WEKA-Explorer. The sample dataset used for this example is the student data available in arff format. Step1: Loading the data. We can load the dataset into weka by clicking on open button in preprocessing interface and selecting the appropriate file. Step2: Once the data is loaded, w ...
... performed using WEKA-Explorer. The sample dataset used for this example is the student data available in arff format. Step1: Loading the data. We can load the dataset into weka by clicking on open button in preprocessing interface and selecting the appropriate file. Step2: Once the data is loaded, w ...
Analysis of the efficiency of Data Clustering Algorithms on high
... tell where the heart of each cluster is located, so that later when presented with an input vector, the system can tell which cluster this vector belongs to by measuring a similarity metric between the input vector and al the cluster centers, and determining which cluster is the nearest or most simi ...
... tell where the heart of each cluster is located, so that later when presented with an input vector, the system can tell which cluster this vector belongs to by measuring a similarity metric between the input vector and al the cluster centers, and determining which cluster is the nearest or most simi ...
Visualizing Outliers - UIC Computer Science
... sensitive to the choice of input parameter values. Most clustering methods are not based on a probability model (see [19] for an exception) so they are susceptible to false negatives and false positives. We will show one remedy in Section 3.3.2. ...
... sensitive to the choice of input parameter values. Most clustering methods are not based on a probability model (see [19] for an exception) so they are susceptible to false negatives and false positives. We will show one remedy in Section 3.3.2. ...
Association Rule Mining
... • The discriminative power of low-support patterns is bounded by a small value. • The discriminative power of high-support patterns is bounded by a small value (e.g. stop words in text classification). Iyad Batal ...
... • The discriminative power of low-support patterns is bounded by a small value. • The discriminative power of high-support patterns is bounded by a small value (e.g. stop words in text classification). Iyad Batal ...
A decision-theoretic approach to data mining
... by , regardless of payoff or class distribution information. Example 2: The following simple example2 is provided to illustrate the above notation. Consider a loan screening application in which applicants for a loan from a bank are classified as one of three classes: “low,” “medium,” or “high” paym ...
... by , regardless of payoff or class distribution information. Example 2: The following simple example2 is provided to illustrate the above notation. Consider a loan screening application in which applicants for a loan from a bank are classified as one of three classes: “low,” “medium,” or “high” paym ...
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.