Grid-Based Mode Seeking Procedure
... the decomposition and the bandwidth is taken as the center of the largest interval over which the number of density attractors remains constant [16]. However, most of these techniques relay on iterative optimization and are computationally expensive. Since in most cases the decomposition is task-dep ...
... the decomposition and the bandwidth is taken as the center of the largest interval over which the number of density attractors remains constant [16]. However, most of these techniques relay on iterative optimization and are computationally expensive. Since in most cases the decomposition is task-dep ...
Data Mining of Machine Learning Performance Data
... particular data may not work well on another. Furthermore, the ‘No Free Lunch’ theorem by Wolpert and Macready [61,page 2] has established that “it is impossible to say that any technique is better than another over the space of all problems. In particular, if algorithm A outperforms algorithm B on ...
... particular data may not work well on another. Furthermore, the ‘No Free Lunch’ theorem by Wolpert and Macready [61,page 2] has established that “it is impossible to say that any technique is better than another over the space of all problems. In particular, if algorithm A outperforms algorithm B on ...
Single Pass Fuzzy C Means
... pointed out in [5] that depending on the size of the data, memory usage can increase significantly as the implementation of Birch has no notion of an allocated memory buffer. In [5], a single pass hard c means clustering algorithm is proposed under the assumption of a limited memory buffer. They use ...
... pointed out in [5] that depending on the size of the data, memory usage can increase significantly as the implementation of Birch has no notion of an allocated memory buffer. In [5], a single pass hard c means clustering algorithm is proposed under the assumption of a limited memory buffer. They use ...
Spatial Generalization and Aggregation of
... of the parts. However, this and similar representations deal with coherent movement of multiple agents, i.e., the case when the agents are moving together as a single unit. This technique is not applicable to independently moving agents, such as cars or pedestrians moving over a city. The term flow ...
... of the parts. However, this and similar representations deal with coherent movement of multiple agents, i.e., the case when the agents are moving together as a single unit. This technique is not applicable to independently moving agents, such as cars or pedestrians moving over a city. The term flow ...
Grid-based Supervised Clustering Algorithm using Greedy and
... clusters that have high data densities [11],[24]. According to them, not only data attribute variables, but also a class variable, take part in grouping or dividing data objects into clusters in the manner that the class variable is used to supervise the clustering. At the end, each cluster is assig ...
... clusters that have high data densities [11],[24]. According to them, not only data attribute variables, but also a class variable, take part in grouping or dividing data objects into clusters in the manner that the class variable is used to supervise the clustering. At the end, each cluster is assig ...
View PDF - CiteSeerX
... Another aspect of feature selection is related to the study of search strategies to which extensive research efforts have been devoted [5,11,41]. The search process starts with either an empty set or a full set. For the former, it expands the search space by adding one feature at a time (Forward Sel ...
... Another aspect of feature selection is related to the study of search strategies to which extensive research efforts have been devoted [5,11,41]. The search process starts with either an empty set or a full set. For the former, it expands the search space by adding one feature at a time (Forward Sel ...
MBPD: Motif-Based Period Detection
... of this work being time series makes it suitable for other kinds of data such as multimedia because they can be converted to time series e.g. the extraction of MFCC from audio as it is used for one of the datasets in our experiments. Several methods have been proposed to detect periods in data. Most ...
... of this work being time series makes it suitable for other kinds of data such as multimedia because they can be converted to time series e.g. the extraction of MFCC from audio as it is used for one of the datasets in our experiments. Several methods have been proposed to detect periods in data. Most ...
A Hybrid Clustering Algorithm for Outlier Detection in Data
... streams. Data stream clustering can be considered as unsupervised learning problem, it deals with finding a structure in a collection of unlabelled data (Aggarwal. et.al., 2004). Hierarchical clustering algorithms recursively nested clusters either in agglomerative method by starting with each data ...
... streams. Data stream clustering can be considered as unsupervised learning problem, it deals with finding a structure in a collection of unlabelled data (Aggarwal. et.al., 2004). Hierarchical clustering algorithms recursively nested clusters either in agglomerative method by starting with each data ...
A Survey on Clustering Algorithm for Microarray Gene Expression
... Neurons that are far apart seem to inhibit each other Neurons seem to have specific non-overlapping tasks The term self-organizing indicates the ability of these NNs to organize the nodes into clusters based on the similarity between them Those nodes that are closer together are more similar than th ...
... Neurons that are far apart seem to inhibit each other Neurons seem to have specific non-overlapping tasks The term self-organizing indicates the ability of these NNs to organize the nodes into clusters based on the similarity between them Those nodes that are closer together are more similar than th ...
Machine Condition Monitoring Using Artificial Intelligence: The
... Machine condition monitoring is gaining importance in industry due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often ...
... Machine condition monitoring is gaining importance in industry due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often ...
How to Use the Fractal Dimension to Find Correlations - ICMC
... Having found a correlation super-group, it is necessary to identify the correlation group where the attribute ak pertains, and dropping attributes that are not part of it. We assume, without loss of generality, that there is no other attribute aj, j
... Having found a correlation super-group, it is necessary to identify the correlation group where the attribute ak pertains, and dropping attributes that are not part of it. We assume, without loss of generality, that there is no other attribute aj, j
DecisionTrees
... of solving complex decision-making tasks. For example, in business, decision trees are used for everything from codifying how employees should deal with customer needs to making high-value investments. In medicine, decision trees are used for diagnosing illnesses and making treatment decisions for i ...
... of solving complex decision-making tasks. For example, in business, decision trees are used for everything from codifying how employees should deal with customer needs to making high-value investments. In medicine, decision trees are used for diagnosing illnesses and making treatment decisions for i ...
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.