In this paper, we develop a new variant of k-means++ seeding that in expectation achieves a constant approximation guarantee. We obtain this result by a simple combination of k-means++ sampling with a local search strategy. We evaluate our algorithm empirically and show that it also improves ...
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In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the 𝒩𝒫NP-hard Set-Union Knapsack Problem...
This book is better as an algorithm reference, and not something you read cover to cover. Can rent it on Kindle Answers: Solutions Errata Algorithm (Jeff Erickson) Write Great Code: Volume 1: Understanding the Machine The book was published in 2004, and is somewhat outdated, but it's ...
While the kNN algorithm has many benefits, it still has some critical problems. Firstly, its classification decision rule is too simple. The kNN algorithm uses the majority voting rule, which means that each of the k-nearest neighbors has one vote in the decision process, ignoring the differenc...
A fast exact k-nearest neighbors algorithm for high dimensional search using k-means clustering and triangle inequality. In The 2011 international joint conference on neural networks, pp. 1293–1299 . https://doi.org/10.1109/IJCNN.2011.6033373 Weber, R., & Blott, S. (1998). A quantitative ...
Consequently, the developed machine learning algorithm, based on well log data, should remain valid for all wells drilled in the Gachsaran formation across the Marun oil field. This means that it can be applied with some confidence as a comprehensive, field-wide, fast, low cost and reliable ...
The traditional A* algorithm suffers from issues such as sharp turning points in the path, weak directional guidance during the search, and a large number of computed nodes. To address these problems, a modified approach called the Directional Search A*
When \(\mu\) is large, it becomes the gradient descent algorithm with a small step size. For this study, the input data were 18 big data monitoring indicators for each city, and the expected output data were their SDG Index scores calculated using statistical data. A total of 254 cities...
However, it may be the case that we do not know k in advance. It will have the limitation of detecting communities by giving clusters the number k. However, the clustering algorithm of a signed social network cannot succeed to obtain the results only by directly extending the theory and ...