Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 2911 for “"clustering"”.
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Symbolic clustering
Clustering is the problem of finding a good organization for data. Because there are many kinds of clustering problems, and because there are many possible clusterings for any data set, clustering programs use knowledge and assumptions about individual problems to make clustering tractable. Cluster …
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Optimised meta-clustering approach for clustering Time Series Matrices
… and efficiently using the newly devised ‘Meta-Clustering’ approach. These time series data gathered from large applications and systems in diverse fields such as communication, medicine, data mining, audio, visual applications, and sensors. The reason time series data was used as the domain of …
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Online clustering with single-pass topology based fuzzy clustering algorithm
Online clustering is of significant interest for real-time data analysis. Generic offline clustering methods such as K-Means, C-Means and others are computationally expensive. The computational burden of these methods increases non-linearly with the size of the data set. In addition these methods …
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Schemas of Clustering
Data mining techniques, such as clustering, have become a mainstay in many applications such as bioinformatics, geographic information systems, and marketing. Over the last decade, due to new demands posed by these applications, clustering techniques have been significantly adapted and extended. …
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Clustering in Multidimensional Spaces with Applications to Statistics Analysis of Earthquake Clustering
… metric is found to follow Weibull distribution. Clustering is defined as deviation from this independence model. An existing declustering program that separates dependent and independent earthquakes into fore- and aftershock clusters is examined. This declustering program is improved with the …
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Unsupervised Morphological Word Clustering
… of morphologically related words). The word clustering is based on clustering of suffixes, which, in turn, is based on stem-suffix co-occurrence frequencies. The suffix clustering is performed as a clique clustering of a weighted undirected graph with the suffixes as vertices; the edges …
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Concept-based Text Clustering
… a crucial task for today's vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that facilitates …
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Neuroengineering of Clustering Algorithms
… divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory …
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Density Based Data Clustering
<p>Data clustering is a data analysis technique that groups data based on a measure of similarity. When data is well clustered the similarities between the objects in the same group are high, while the similarities between objects in different groups are low. The data clustering technique is widely …
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Faster k-means clustering.
The popular k-means algorithm is used to discover clusters in vector data automatically. We present three accelerated algorithms that compute exactly the same clusters much faster than the standard method. First, we redesign Hamerly's algorithm to use k heaps to avoid checking distance bounds for …
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Incremental clustering for trajectories
Trajectory clustering has played a crucial role in data analysis since it reveals underlying trends of moving objects. Due to their sequential nature, trajectory data are often received incrementally, e.g., continuous new points reported by GPS system. However, since existing trajectory clustering …
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Clustering in high dimensions
Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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Multi-source contingency clustering
This thesis examines the problem of clustering multiple, related sets of data simultaneously. Given datasets which are in some way connected (e.g. temporally) but which do not necessarily share label compatibility, we exploit co-occurrence in- formation in the form of normalized multidimensional …
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Clustering via matrix exponentiation
… for only a single squaring. It is shown that the clustering performance of the algorithm degrades with larger values of the exponent, thus revealing that a single squaring is optimal.
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New algorithms for EST clustering
… for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology. There are a few systems, designed for this purpose and a few more are currently under development. These …
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External Support Vector Machine Clustering
The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data …
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Clustering analysis using Swarm Intelligence
… application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear …
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Increasing DOGMA Scaling Through Clustering
DOGMA is a distributed computing architecture developed at Brigham Young University. It makes use of idle computers to provide additional computing resources to applications, similar to Seti@home. DOGMA's ability to scale to large numbers of computers is hindered by its strict client-server …
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Clustering of Database Query Results
… users information overload can be reduced by clustering database query results. A hierarchical agglomerative clustering algorithm is used to cluster the query results. The reduction of users information overload is evaluated using Chakrabarti et al information overload cost model. Empirical …
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Clustering strategies for object databases
… is the general area of concern for this thesis. Clustering is one fruitful design technique which can provide improvements in performance. However, clustering in object databases has not been explored in depth and so has not been truly exploited. Further, clustering, although a physical concern, …
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