Automatic Mood Classication of Indian Popular Music
... proposed in the literature for music classification. Different taxonomies exist for the categorization of audio features. Weihs et al. [40] have categorized the audio features into four subcategories, namely short-term features, long-term features, semantic features, and compositional features. Scar ...
... proposed in the literature for music classification. Different taxonomies exist for the categorization of audio features. Weihs et al. [40] have categorized the audio features into four subcategories, namely short-term features, long-term features, semantic features, and compositional features. Scar ...
A feature group weighting method for subspace clustering of high
... the optimization process in which two types of subspace weights are introduced. We propose a new iterative algorithm FG-k-means to optimize the optimization model. The new algorithm is an extension to k-means, adding two additional steps to automatically calculate the two types of subspace weights. ...
... the optimization process in which two types of subspace weights are introduced. We propose a new iterative algorithm FG-k-means to optimize the optimization model. The new algorithm is an extension to k-means, adding two additional steps to automatically calculate the two types of subspace weights. ...
Nonnegative Matrix Factorization with Sparseness Constraints
... How can we combine these ideas? ...
... How can we combine these ideas? ...
Test - UF CISE - University of Florida
... – Leaf nodes, each of which have exactly one incoming Source: Data Mining – Introductory and Advanced topics by Margaret Dunham edge and no outgoing edges. Each leaf node also Leaf node has a class label attached to it Data Mining Sanjay Ranka Spring 2011 ...
... – Leaf nodes, each of which have exactly one incoming Source: Data Mining – Introductory and Advanced topics by Margaret Dunham edge and no outgoing edges. Each leaf node also Leaf node has a class label attached to it Data Mining Sanjay Ranka Spring 2011 ...
HOT: Hypergraph-based Outlier Test for Categorical Data
... usually interesting for helping the decision makers to make profit or improve the service quality. A descriptive definition of outliers is given by Hawkins like this:”an outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a differe ...
... usually interesting for helping the decision makers to make profit or improve the service quality. A descriptive definition of outliers is given by Hawkins like this:”an outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a differe ...
Privacy-Preserving Classification of Customer Data without Loss of
... sions of randomization of data. In contrast, cryptographic solutions to privacypreserving data mining that provide strong privacy have been proposed [LP02, VC02, VC03, KC02, WY04], but these tend to do so at a high performance cost. In particular, in order to have efficiency that is reasonable (say ...
... sions of randomization of data. In contrast, cryptographic solutions to privacypreserving data mining that provide strong privacy have been proposed [LP02, VC02, VC03, KC02, WY04], but these tend to do so at a high performance cost. In particular, in order to have efficiency that is reasonable (say ...
Constraint Based Periodicity Mining in Time Series Databases
... Cheung [5] used suffix tree similar to STNR [26] which is not beneficial in terms of growth of tree. Huang and Chang [16] and STNR [26] presented their algorithm for finding periodic patterns, with allowable range along the time axis. Both finds all type of periodicity by utilizing the time toleranc ...
... Cheung [5] used suffix tree similar to STNR [26] which is not beneficial in terms of growth of tree. Huang and Chang [16] and STNR [26] presented their algorithm for finding periodic patterns, with allowable range along the time axis. Both finds all type of periodicity by utilizing the time toleranc ...
Mining Frequent Patterns from Very High Dimensional Data: A Top
... gene networks [9]. Classification and clustering algorithms are also applied on microarray data [3, 4, 6]. Although there are many algorithms dealing with transactional data sets that usually have a small number of dimensions and a large number of tuples, there are few algorithms oriented to very hi ...
... gene networks [9]. Classification and clustering algorithms are also applied on microarray data [3, 4, 6]. Although there are many algorithms dealing with transactional data sets that usually have a small number of dimensions and a large number of tuples, there are few algorithms oriented to very hi ...
Optimizing metric access methods for querying and mining complex
... The development of approaches to make data clustering algorithms, which are based on similarity comparisons, feasible for large-scale datasets has also been pursued for the data mining research community over the last decades. Among them, the use of sampling techniques has proved to be especially us ...
... The development of approaches to make data clustering algorithms, which are based on similarity comparisons, feasible for large-scale datasets has also been pursued for the data mining research community over the last decades. Among them, the use of sampling techniques has proved to be especially us ...
Communication-Efficient Privacy-Preserving Clustering
... Bunn and Ostrovsky [7]. Oliviera and Zaı̈ane’s work [41] uses data transformation in conjunction with partition-based and hierarchical clustering algorithms, while the others use cryptographic techniques to give privacy-preserving versions of the k-means clustering algorithm. Vaidya and Clifton’s re ...
... Bunn and Ostrovsky [7]. Oliviera and Zaı̈ane’s work [41] uses data transformation in conjunction with partition-based and hierarchical clustering algorithms, while the others use cryptographic techniques to give privacy-preserving versions of the k-means clustering algorithm. Vaidya and Clifton’s re ...
Wavelength management in WDM rings to maximize the
... ≈ 1.49015, improving on the 1.58198 bound obtained by a simple iterative algorithm. Again, we use as a subroutine an algorithm of Carlisle and Lloyd [4] for solving the profit variant of maxPC in chains. For the analysis of the algorithms for the non-profit version of the problems, we develop a new ...
... ≈ 1.49015, improving on the 1.58198 bound obtained by a simple iterative algorithm. Again, we use as a subroutine an algorithm of Carlisle and Lloyd [4] for solving the profit variant of maxPC in chains. For the analysis of the algorithms for the non-profit version of the problems, we develop a new ...
Document
... KDDCUP-2000 received 30 entrants (teams) attempting to mine knowledge from electronic-commerce data. As reported by Brodley and Kohavi [Brodley & Kohavi, 2000], most types of data-mining algorithm were tried by only a small fraction of participants. There are several reasons why even expert data min ...
... KDDCUP-2000 received 30 entrants (teams) attempting to mine knowledge from electronic-commerce data. As reported by Brodley and Kohavi [Brodley & Kohavi, 2000], most types of data-mining algorithm were tried by only a small fraction of participants. There are several reasons why even expert data min ...
Data Mining with Weka - Department of Computer Science
... Lesson 1.2: Exploring the Experimenter Use the Experimenter for … determining mean and standard deviation performance of a classification algorithm on a dataset … or several algorithms on several datasets Is one classifier better than another on a particular dataset? … and is the difference sta ...
... Lesson 1.2: Exploring the Experimenter Use the Experimenter for … determining mean and standard deviation performance of a classification algorithm on a dataset … or several algorithms on several datasets Is one classifier better than another on a particular dataset? … and is the difference sta ...
A Comparative Study of Discretization Methods for Naive
... are discretized into two intervals and the resulting class information entropy is calculated. A binary discretization is determined by selecting the cut point for which the entropy is minimal amongst all candidates. The binary discretization is applied recursively, always selecting the best cut poin ...
... are discretized into two intervals and the resulting class information entropy is calculated. A binary discretization is determined by selecting the cut point for which the entropy is minimal amongst all candidates. The binary discretization is applied recursively, always selecting the best cut poin ...
Ch 9.2.1
... maximizing “the shared neighbors” objective function Assign the remaining points to the clusters that have been found ...
... maximizing “the shared neighbors” objective function Assign the remaining points to the clusters that have been found ...
Optimal Solution for Santa Fe Trail Ant Problem using MOEA
... Simulated annealing can be used with NSGA II, where a probabilistic approach applied to making faster solution category with some sort of probability of solution optimality. Scatter search technique is applied to evolutionary algorithm of hard-optimization problems.it works on search space where dif ...
... Simulated annealing can be used with NSGA II, where a probabilistic approach applied to making faster solution category with some sort of probability of solution optimality. Scatter search technique is applied to evolutionary algorithm of hard-optimization problems.it works on search space where dif ...
12 On-board Mining of Data Streams in Sensor Networks
... for a number of samples into 2k and this process is repeated to a number of levels, and finally it clusters the 2k clusters to k clusters. Babcock et al. [8] have used an exponential histogram (EH) data structure to enhance the Guha et al. algorithm. They use the same algorithm described above, howev ...
... for a number of samples into 2k and this process is repeated to a number of levels, and finally it clusters the 2k clusters to k clusters. Babcock et al. [8] have used an exponential histogram (EH) data structure to enhance the Guha et al. algorithm. They use the same algorithm described above, howev ...
Comparative Study of Clustering Techniques
... clustering algorithms dermatology dataset is used which contains 35 attributes and 366 instances. Study comprises of different data mining clustering algorithms, i.e. KMeans, Hierarchical clustering, Density based clustering algorithm, Farthest first clustering and Filtered Clustering for comparison ...
... clustering algorithms dermatology dataset is used which contains 35 attributes and 366 instances. Study comprises of different data mining clustering algorithms, i.e. KMeans, Hierarchical clustering, Density based clustering algorithm, Farthest first clustering and Filtered Clustering for comparison ...
Exact Primitives for Time Series Data Mining
... 2.10 Comparison of the number of times ptolemaic bound prunes a distance computation to that of linear bound for various values of n and m . . . . . . . . . 34 2.11 (top) A segment of ECG with a query. (middle) All the twelve beats are detected. Plotting the z-normalized distance from the query to t ...
... 2.10 Comparison of the number of times ptolemaic bound prunes a distance computation to that of linear bound for various values of n and m . . . . . . . . . 34 2.11 (top) A segment of ECG with a query. (middle) All the twelve beats are detected. Plotting the z-normalized distance from the query to t ...
Advances in Natural and Applied Sciences
... interoperable between WEKA and OWL, the blank spaces in the attribute names are removed. This dataset contains some missing values. The existing classifiers itself had procedure for handling the missing values. In the case of J48 classifier, any split on an attribute with missing value will be done ...
... interoperable between WEKA and OWL, the blank spaces in the attribute names are removed. This dataset contains some missing values. The existing classifiers itself had procedure for handling the missing values. In the case of J48 classifier, any split on an attribute with missing value will be done ...
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.