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.
Results
Showing 1 to 20 of 384 for “"k-means"”.
-
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 …
-
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 …
-
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 …
-
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, …
-
Private k-means clustering : algorithms and applications
… 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 that is both differentially private, and has an approximation error that depends sub-linearly on the data’s …
-
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]) …
-
K-means initialisation algorithms: an extensive comparative study
The K-means data clustering algorithm, whilst widely popular, is not without its drawbacks. In this work, we are particularly interested in the sensitivity of K-means to its initialisation, in the form of a set of initial centroids. Since the cluster recovery performance of K-means can potentially …
-
A fast seeding technique for k-means algorithm.
The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers …
-
Statistical and algorithmic foundation of K-means clustering
… 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 clustering enjoys the strongest statistical …
-
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 …
-
Geometric methods of accelerating triangle-inequality-based k-means.
… the most frequent ways how to cluster data is k-means. The standard way of solving the problem is iterative Lloyd's algorithm. This algorithm performs many redundant calculations. Elkan's and Hamerly's algorithms, the heap algorithm and many others eliminate this redundancy by maintaining a set …
-
Scalable second-order Riemannian optimization for K-means clustering
… 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 statistically solvable. In this …
-
Optimizing parameters in fuzzy k-means for clustering microarray data.
… hierarchical clustering, self-organizing maps, k-means clustering and fuzzy k-means clustering, have become important tools for expression analysis of microarray data. However, the need of prior knowledge of the number of clusters, k, and the fuzziness parameter, b, limits the usage of fuzzy …
-
Optimal Clustering: Genetic Constrained K-Means and Linear Programming Algorithms
… this dissertation, we propose two constrained k-means algorithms: Linear Programming Algorithm (LPA) and Genetic Constrained K-means Algorithm (GCKA). Linear Programming Algorithm modifies the k-means algorithm into a linear programming problem with constraints requiring that each cluster have m …
-
K-means landscapes: exploring clustering solution spaces using energy landscape theory
K-means, one of the simplest clustering algorithms, is ubiquitous in every scientific field. Its cost function supports many possible clustering solutions, and location of low-valued clustering solutions can be challenging. Hence, the topography of the cost function surface is crucial to …
-
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.
-
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 …
-
An automatic email mining approach using semantic non-parametric K-Means++ clustering
… a novel approach named Semantic Non-parametric K-Means++ clustering is proposed for folder creation, which avoids, (1) random seed selection by selecting the seed according to email weights, and (2) pre-defined number of clusters using the similarity between the email contents. Experiments show …
-
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 …
-
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 …
Page 1 of 20