Ensemble-Initialized k-Means Clustering

Shasha, Xu; Huang, Dong

doi:10.1145/3318299.3318308

Cited by 15 publications

(15 citation statements)

References 27 publications

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“…Although one could plausibly use the allele frequencies of the SKFR clusters as initial values, hard clustering sometimes produces small clusters with extreme frequencies. More advanced techniques such as power K -means 39 may well yield even better initialization.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Chu

Peterson

et al. 2022

Preprint

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Admixture estimation plays a crucial role in ancestry inference and genomewide association studies (GWAS). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 10^5 to 10^6 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank data sets. Our implementation of the method is called OpenADMIXTURE.

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Section: Methodsmentioning

confidence: 99%

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Chu

Peterson

et al. 2022

Preprint

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“…Within multi-agent reinforcement learning there have been a variety of approaches inspired by ToM reasoning, modeling beliefs (Fuchs et al, 2021;Wang et al, 2022;Sclar et al, 2022) and intents (Qi & Zhu, 2018;Xu et al, 2019). Other inverse reinforcement learning methods approach ToM-like reasoning by conditioning the reward function on inferred latent characteristics (Tian et al, 2021;Wu et al, 2023).…”

Section: Theory Of Mind In Multi-agent Rlmentioning

confidence: 99%

Explainable Action Advising for Multi-Agent Reinforcement Learning

Guo

Campbell

Stepputtis

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics induced in multi-agent settings. We present a method of grounding semantically meaningful, human-interpretable beliefs within policies modeled by deep networks. We then consider the task of 2nd-order belief prediction. We propose that ability of each agent to predict the beliefs of the other agents can be used as an intrinsic reward signal for multi-agent reinforcement learning. Finally, we present preliminary empirical results in a mixed cooperativecompetitive environment.

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“…If the number is small, the data items in each cluster will differ greatly, and it is difficult to select the local optimal point, which makes it impossible to achieve the global optimal partition. K-means, 47 Clarans 48 and CHAMELEON 49 algorithms are classical algorithms of hierarchical clustering.…”

Section: Clustering and Related Technologiesmentioning

confidence: 99%

A privacy‐preserving recommendation method with clustering and locality‐sensitive hashing

Zhang

et al. 2022

Computational Intelligence

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Nowadays, there is a significant increase in information, resulting in information overload. Recommendation systems have been widely adopted, and they can help users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. To solve problems of data sparseness, enormous high‐dimensional data, the cold start problem and privacy protection in an intelligent recommender system, this study proposes a privacy‐preserving collaborative filtering recommendation method with clustering and locality‐sensitive hashing. First, we cluster users according to their characteristic information to obtain sub‐rating matrices. We use the latent factor model to predict and fill in the missing ratings in those matrices. Second, we combine the sub‐rating matrices into a complete rating matrix, subsequently, we obtained the neighbors of the target user by analyzing the similarity of the users. We use a locality‐sensitive hashing algorithm to reduce the dimensionality of the user rating data and build an index that could quickly obtain the neighbors of the target user. Finally, we predict the target user's ratings and provide recommendations to the target user. Through experiments, our study shows that our method can deal with the problems of data sparseness and cold start problems well and the accuracy of the intelligent recommendation system has been improved. In addition, we use hash techniques to search for the neighbors, which effectively protects the privacy of the user.

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Ensemble-Initialized k-Means Clustering

Cited by 15 publications

References 27 publications

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Explainable Action Advising for Multi-Agent Reinforcement Learning

A privacy‐preserving recommendation method with clustering and locality‐sensitive hashing

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