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 70 for “"Data clustering"”.
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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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Data Clustering And Visualization Through Matrix Factorization
<p>Clustering is traditionally an unsupervised task which is to find natural groupings or clusters in multidimensional data based on perceived similarities among the patterns. The purpose of clustering is to extract useful information</p> <p>from unlabeled data.</p> <p>In order to present the …
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Multi-objective evolutionary algorithms for data clustering
… use of Multi-Objective metaheuristics for the data-mining task of clustering. We �first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality and then perform …
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APPLY DATA CLUSTERING TO GENE EXPRESSION DATA
<p>Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the …
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Isotropy criteria and algorithms for data clustering
Given a set of points, the goal of data clustering is to group them into clusters, such that the internal homogeneity of points within each cluster contrasts to inter-cluster heterogeneity. Over the last fifty years, many methods for data clustering have been developed in diverse scientific …
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Dynamic Online Data Clustering for Object -Oriented Databases
… the benefits and the penalties of dynamic online clustering, and database system parameters. The shortcomings of the existing implementations are discovered by qualitatively analyzing the resource consumption of each dynamic online clustering component, and then improvements are developed based on …
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How to Use K-means for Big Data Clustering?
K-means plays a vital role in data mining, being the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data. Therefore, it is crucial to improve K-means by scaling it …
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Microarray time-series data clustering via gene expression profile alignment
Clustering gene expression data given In terms of time-series is a challenging problem that imposes its own particular constraints, namely, exchanging two or more time points is not possible as it would deliver quite different results and would lead to erroneous biological conclusions. In this …
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Advances in nonnegative matrix factorization with application on data clustering.
Clustering is an important direction in many fields, e.g., machine learning, data mining and computer vision. It aims to divide data into groups (clusters) for the purposes of summarization or improved understanding. With the rapid development of new technology, high-dimensional data become very …
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A Data Clustering Approach to Support Modular Product Family Design
… platform concepts and product variants using a data clustering approach. A case application developed in collaboration with a tire manufacturer is used to verify that this research approach is suitable for reducing the complexity of design results by determining design commonalities across …
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Partitioning A Graph In Alliances And Its Application To Data Clustering
… similarities). Discovery of such structure in a data set is called clustering or unsupervised learning and the ability to do it automatically is desirable for many applications in the areas of pattern recognition, computer vision, artificial intelligence, behavioral and social sciences, life …
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An improved self organizing map using jaccard new measure for textual bugs data clustering
In software projects there is a data repository which contains the bug reports. These bugs are required to carefully analyze to resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the delaying in addressing some important bugs resolutions. To …
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Proposing an ensemble-based model using data clustering and machine learning algorithms for effective predictions
… tasks in machine learning is prediction. Data scientists use various regression methods to find the most appropriate and accurate model applicable for each type of datasets. This study proposes a meta-model to improve prediction accuracy. In common methods different models are applied to …
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Statistical Methods for Multivariate Functional Data Clustering, Recurrent Event Prediction, and Accelerated Degradation Data Analysis
… project concentrates on the multivariate sensory data, the second project is related to the bivariate recurrent process, and the third project introduces thermal index (TI) estimation in accelerated destructive degradation test (ADDT) data, in which an R package is developed. All three projects …
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A generalization based hybrid algorithm for clustering semi-structured data
… construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data …
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Investigation of machine learning tools for document clustering and classification
Data clustering is a problem of discovering the underlying data structure without any prior information about the data. The focus of this thesis is to evaluate a few of the modern clustering algorithms in order to determine their performance in adverse conditions. Synthetic Data Generation software …
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Multivariate Analysis for Scanning (Transmission) Electron Diffraction
… aberration correction, signal collection, and data processing have all improved in recent years, leading to a move away from the acquisition single-variable images. Instead it is increasingly common, in the field of analytical S(T)EM, for whole energy spectra to be collected for post-facto …
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SimNet: a neural network architecture for pattern recognition and data mining
… concept of fuzzy logic to produce a rapid data clustering system that works similar to Adaptive Resonance Theory and Self-Organizing Maps."--Introduction, page 1.
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Uncertainties in gender violence epidemiology
… gender violence epidemiology. These are missing data, clustering and unrecognised causal relationships. In this thesis I ask: Can we reduce these three uncertainties in gender violence epidemiology? A systematic review of the intimate partner violence literature over the last decade found that …
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Determination of drivers of stock-out performance of retail stores using data mining techniques
This research applies data mining techniques to give a picture of the interaction of performance variables such as between stock-outs and store attributes, and stock-outs and other variables including store sales, income and demographic data, as well as various aspects of inventory management data. …
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