Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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01 Jan 1991
TL;DR: In this article, a technique for directly designing a variable-rate tree-structured vector quantizer by growing the tree one node at a time rather than one layer at time is presented.
Abstract: A technique for directly designing a variable-rate tree-structured vector quantizer by growing the tree one node at a time rather than one layer at time is presented. The technique is a natural extension of a tree growing method for decision trees. When the tree is pruned with a generalized algorithm for optimally pruning trees, improvement is measured in the signal-to-noise ratio at high rates over pruning a fixed-rate tree-structured vector quantizer of the same initial rate. The growing algorithm can be interpreted as a constrained inverse of the pruning algorithm. >
127 citations
20 Aug 2006
TL;DR: This paper examines the evaluation functions for measuring the combined significance of a pattern set and proposes the MMS (Maximal Marginal Significance) as the problem formulation and presents a greedy algorithm which approximates the optimal solution with performance bound O(log k) (with conditions on redundancy), where k is the number of reported patterns.
Abstract: Observed in many applications, there is a potential need of extracting a small set of frequent patterns having not only high significance but also low redundancy. The significance is usually defined by the context of applications. Previous studies have been concentrating on how to compute top-k significant patterns or how to remove redundancy among patterns separately. There is limited work on finding those top-k patterns which demonstrate high-significance and low-redundancy simultaneously.In this paper, we study the problem of extracting redundancy-aware top-k patterns from a large collection of frequent patterns. We first examine the evaluation functions for measuring the combined significance of a pattern set and propose the MMS (Maximal Marginal Significance) as the problem formulation. The problem is known as NP-hard. We further present a greedy algorithm which approximates the optimal solution with performance bound O(log k) (with conditions on redundancy), where k is the number of reported patterns. The direct usage of redundancy-aware top-k patterns is illustrated through two real applications: disk block prefetch and document theme extraction. Our method can also be applied to processing redundancy-aware top-k queries in traditional database.
127 citations
03 Apr 2013
TL;DR: A general Variable Neighborhood Search with an embedded Variable Neighborhood Descent that exploits a series of neighborhood structures that yields good solutions and scales much better to larger instances than two mixed integer programming approaches.
Abstract: We consider the necessary redistribution of bicycles in public bicycle sharing systems in order to avoid rental stations to run empty or entirely full. For this purpose we propose a general Variable Neighborhood Search (VNS) with an embedded Variable Neighborhood Descent (VND) that exploits a series of neighborhood structures. While this metaheuristic generates candidate routes for vehicles to visit unbalanced rental stations, the numbers of bikes to be loaded or unloaded at each stop are efficiently derived by one of three alternative methods based on a greedy heuristic, a maximum flow calculation, and linear programming, respectively. Tests are performed on instances derived from real-world data and indicate that the VNS based on a greedy heuristic represents the best compromise for practice. In general the VNS yields good solutions and scales much better to larger instances than two mixed integer programming approaches.
127 citations
TL;DR: Wang et al. as mentioned in this paper considered a multi-skill spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a skill set.
Abstract: With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers’ benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, $g$ -divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
126 citations
TL;DR: It is proved that no such approach to evading the CAP can work, and a new, fast algorithm is given for learning unions of half spaces in fixed dimension, suggesting a generalization of this approach which naively would avoid a credit assignment problem and learn in time polynomial in dimension.
126 citations