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 196 for “"K-means Clustering"”.
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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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Local Optima in K-Means Clustering
… study of the properties of local optimality in K-means clustering is pursued. In doing so, it is shown that several of the commercial software packages prove to be inadequate in their treatment of the K-means algorithm, resulting in the proposal of an alternative method based on several thousand …
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Query K-means clustering for crowdsourcing
This thesis focuses on solving the $K$-means clustering problem approximately with side information provided by crowdsourcing. Both binary same-cluster oracle and general crowdsourcing framework are considered. It can be shown that, under some mild assumptions on the smallest cluster size, one can …
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Dimensionality reduction for k-means clustering
… reduction techniques for approximate k-means clustering. Given a large dataset, we consider how to quickly compress to a smaller dataset (a sketch), such that solving the k-means clustering problem on the sketch will give an approximately optimal solution on the original dataset. First, …
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Private k-means clustering : algorithms and applications
… applying differential privacy algorithms for clustering points on real databases. This thesis describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, it gives the first 𝑘-means clustering algorithm …
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Approximate k-means clustering through random projections
… Importantly, this class of problems includes k-means clustering. By reducing data points to just O(k) dimensions, our methods generically accelerate any exact, approximate, or heuristic algorithm for these ubiquitous problems. For k-means dimensionality reduction, we provide (1+ [epsilon]) …
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Statistical and algorithmic foundation of K-means clustering
Clustering is a widely deployed unsupervised learning tool. Given data in the Euclidean spoace, K-means clustering is one of the most commonly used clustering methods, which minimize the distance between each point to the centroid of its assigned cluster. Among the popular clustering methods, SDP …
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Scalable second-order Riemannian optimization for K-means clustering
Clustering is a fundamental problem in unsupervised learning. The classical K-means formulation for clustering is a worst-case NP-hard discrete optimization problem. Despite being NP-hard, the SDP relaxation of the discrete formulation is guaranteed to recover the true cluster whenever it is …
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Identifying boosted objects with N-subjettiness and linear k-means clustering
… minimization procedure which I call the linear k-means clustering algorithm. As a true jet shape with highly effective tagging performances, N-subjettiness has many advantages on the experimental as well as on the theoretical side.
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Cyclic exchange neighborhood search technique for the K-means clustering problem
… neighborhood search technique for the k-means clustering problem. Neighbors of a feasible solution are obtained by moving points between clusters in a cycle. This method attempts to improve local minima obtained by the well-known Lloyd's algorithm. Although the results did not establish …
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An automatic email mining approach using semantic non-parametric K-Means++ clustering
… email mining systems such as Kernel-Selected clustering and BuzzTrack, do not consider the semantic similarity between email contents, also when large number of email messages are clustered to a single folder they retain the problem of email overload. This thesis proposes a system named AEMS …
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Indoor Radio Dot Placement Optimization using UE Positioning and K-Means Clustering
… the Machine Learning (ML) cluster algorithm of K-means being used to evaluate the ideal scenario where all the RD unit locations are adjusted. Further thesis specific algorithms are used to improve network performance for a cost-efficient solution is implemented. Results have proved that dynamic …
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Malaysia household incomes classification prediction with k-means clustering and fuzzy inference system
… using fuzzy inference system (FIS) from the K-means clustering outputs. Thus, this study focuses on three main objectives which are (a) To apply K-means clustering on household incomes data, (b) To propose the prediction of household incomes classification using FIS, and (c) To analyze and …
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An Arithmetic-Based Deterministic Centroid Initialization Method for the k-Means Clustering Algorithm
<p>One of the greatest challenges in k-means clustering is positioning the initial cluster centers, or centroids, as close to optimal as possible, and doing so in an amount of time deemed reasonable. Traditional fc-means utilizes a randomization process for initializing these centroids, and poor …
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Automated Parsing of Flexible Molecular Systems using Principal Component Analysis and K-Means Clustering Techniques
… (ii) use principal component analysis (PCA) and clustering to find and investigate conformational families within the ensemble, (iii) separate and visualize conformational families in a user-friendly manner, and (iv) convey to the user how conformational families were delineated by way of …
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A comparison of driving characteristics and environmental characteristics using factor analysis and k-means clustering algorithm
… identified different driver types using k-means clustering, and studied how the same drivers map in each classification domain. The research consists of two study cases. In the first study case, a new variable is proposed and then is used for classification. The drivers were divided into …
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Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems
… Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means clustering to partition the entire dataset reduces the time complexity and improves the online scalability as well as the data density. Moreover, the similarity comparison …
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Unsupervised machine learning and k-Means clustering as a way of discovering anomalous events In continuous seismic time series
Unsupervised k-Means clustering was implemented as a method for identifying anomalies in seismic time series. Sliding window approach was used for generating specific subsequences from the overall waveform. Dynamic Time Warping (DTW) was used as the method for comparing seismic subsequences. DTW …
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Towards an epidemiology of gentrification : modeling urban change as a probabilistic process using k-means clustering and Markov models
Gentrification is viewed as both as a tool and a force--as a systematized vehicle for classbased oppression and racism, and an empirical force of change based on social, environmental and economic interactions. This complexity makes it challenging for researchers to study the impact of …
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