Clustering by Hill-Climbing: Consistency Results
We consider several hill-climbing approaches to clustering as formulated by Fukunaga and Hostetler in the 1970's. We study both continuous-space and discrete-space (i.e., medoid) variants and establish their consistency.
Code (1)
Tasks
ClusteringSimilar Papers 제목 키워드 기반
Graph Max Shift: A Hill-Climbing Method for Graph Clustering
We present a method for graph clustering that is analogous with gradient ascent methods previously proposed for clustering points in space. We show that, when applied to a random geometric graph with data iid from some d…
ClusteringGraph ClusteringBandit-Based Random Mutation Hill-Climbing
The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it…
A Simple Approach to Sparse Clustering
Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in …
ClusteringHybrid Genetic Algorithm and Hill Climbing Optimization for the Neural Network
In this paper, we propose a hybrid model combining genetic algorithm and hill climbing algorithm for optimizing Convolutional Neural Networks (CNNs) on the CIFAR-100 dataset. The proposed model utilizes a population of c…
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
It has been shown in the past that a multistart hillclimbing strategy compares favourably to a standard genetic algorithm with respect to solving instances of the multimodal problem generator. We extend that work and ver…
Diversity