Article
Computer Science, Artificial Intelligence
Carlo Baldassi
Summary: We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with a state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly but generally better at escaping local minima Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example).
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yi-Cheng Chen, Yen-Liang Chen, Jyun-Yun Lu
Summary: K-Means algorithm is one of the most famous and popular clustering algorithms in the world, known for its simple structure, easy implementation, high efficiency, and fast convergence speed. This article introduces an improvement to past variants of K-Means used in evolutionary clustering, considering both past and future clustering results, and extending K-Means to multiple cycles, resulting in more consistent, stable, and smooth clustering results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Uri Stemmer
Summary: This research presents a new algorithm operating in the local model of differential privacy for solving the Euclidean k-means problem, significantly reducing additive error while maintaining multiplicative error. The study shows that the obtained additive error in handling the k-means objective is almost optimal in terms of its dependency on the database size.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Ahmed Fahim
Summary: The k-means method divides N objects into k clusters based on mean values, with linear time complexity and dependence on knowing the number of clusters and initial centers. This research introduces a method able to detect near-optimal values for k and initial centers without prior knowledge, resulting in improved final result quality. The proposed method combines DBSCAN and k-means to converge to global minima and has a time complexity of o(n log n).
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Computer Science, Information Systems
Jing Liu, Fuyuan Cao, Jiye Liang
Summary: In this paper, a centroids-guided deep multi-view k-means clustering method is proposed, which incorporates deep representation learning into the multi-view k-means objective. The method produces more k-means-friendly representations by reducing the loss between each representation and its assigned cluster centroid.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hongfu Liu, Junxiang Chen, Jennifer Dy, Yun Fu
Summary: K-means is a widely used clustering algorithm known for its simplicity and efficiency. This review paper focuses on generalizing K-means to solve challenging and complex problems. It unifies the available approaches in terms of data representation, distance measure, label assignment, and centroid updating. Concrete applications of modified K-means formulations are reviewed, including iterative subspace projection and clustering, consensus clustering, constrained clustering, domain adaptation, and outlier detection.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Avgoustinos Vouros, Stephen Langdell, Mike Croucher, Eleni Vasilaki
Summary: K-Means is a widely used algorithm for data clustering, but it has limitations such as only finding local minima and being sensitive to initial centroid positions. Various K-Means variations and initialization techniques have been proposed, with more sophisticated techniques reducing the need for complex clustering methods. Deterministic methods generally outperform stochastic methods, but there is a trade-off where simpler stochastic methods run multiple times can result in better clustering.
Article
Computer Science, Artificial Intelligence
Luc Giffon, Valentin Emiya, Hachem Kadri, Liva Ralaivola
Summary: K-means algorithm and Lloyd's algorithm have expanded beyond their original clustering purposes to play pivotal roles in various machine learning and data analysis techniques. QuicK-means is an efficient extension of K-means that reduces computational complexity through sparse matrix products, demonstrating benefits through experimental results.
Article
Computer Science, Artificial Intelligence
Peter Olukanmi, Fulufhelo Nelwamondo, Tshilidzi Marwala
Summary: A key drawback of k-means algorithm is its susceptibility to local minima. The authors propose a technique for comparing initializations directly and selecting the best one based on the maximum minimum inter-center distance. The experiments and mathematical analysis show significant efficiency gains and improved accuracy compared to repeated k-means.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Psychology, Multidisciplinary
Cheol Young Kim, Hyun Sun Chung
Summary: The purpose of this study is to examine the trajectory of change in job satisfaction and job burnout for workers with union membership from the perspective of the conservation of resources theory. The study fills a gap in the literature regarding the direction of the union membership effect. The results indicate that union workers develop negative psychological state more quickly and have a larger cross-lagged effect compared to nonunion workers.
CURRENT PSYCHOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Marco Capo, Aritz Perez, Jose A. Antonio
Summary: The K-means algorithm is a popular clustering method, but its performance depends heavily on the initialization phase. Researchers have developed various initialization techniques to address this issue. This article introduces a cost-effective Split-Merge step that can restart the K-means algorithm after reaching a fixed point, reducing error and computing fewer distances.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Miaomiao Li, Yi Zhang, Suyuan Liu, Zhe Liu, Xinzhong Zhu
Summary: Multiple kernel clustering (MKC) aims to determine the optimal kernel from several pre-computed basic kernels. A new algorithm called simple multiple kernel k-means with kernel weight regularization (SMKKM-KWR) is proposed to overcome the issue of sparse or over-selected kernel weight coefficients. Experimental results show that SMKKM-KWR achieves effective and efficient clustering performance.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Simon Harris, Renato Cordeiro De Amorim
Summary: This paper compares the performance of 17 different algorithms on 6,000 synthetic and 28 real-world data sets to investigate the sensitivity of k-means to its initial centroids. The results show that different algorithms may excel in different clustering scenarios, providing valuable insights for those considering k-means for complex clustering tasks.
Article
Computer Science, Interdisciplinary Applications
Rasim M. Alguliyev, Ramiz M. Aliguliyev, Lyudmila Sukhostat
Summary: This article introduces a new parallel batch clustering algorithm based on the k-means algorithm, which reduces computation complexity by splitting the dataset into multiple partitions and proposes a method to determine the optimal batch size. Experimental results show the practical applicability of this method for handling Big Data.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Chenhui Gao, Wenzhi Chen, Feiping Nie, Weizhong Yu, Feihu Yan
Summary: In this paper, we propose two algorithms, FDKM and IFDKM, for clustering high-dimensional data in a low-dimensional subspace. These algorithms have higher efficiency and lower time complexity compared to traditional methods, and their superior performance is demonstrated in multiple experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Public, Environmental & Occupational Health
Samantha Horn, Yana Litovsky, George Loewenstein
Summary: This study suggests that curiosity can be a useful tool in increasing demand for and engagement with aversive health information. By manipulating curiosity through various methods, researchers found that participants were more likely to view and engage with information about their drinking habits, cancer risk, and the sugar content in drinks. Overall, curiosity prompts provide a simple and effective way to increase engagement with aversive health information.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Sandra Gillner
Summary: Despite high expectations, the extensive and rapid adoption of AI in medical diagnostics has not been realized. This study investigates the perception and navigation of AI providers in complex healthcare systems, revealing their self-organization to increase adaptability and the practices utilized to mitigate tensions within the healthcare subsystems.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Fabian Duartea, Alvaro Jimenez-Molina
Summary: This study found that violence related to social protest has a significant impact on depressive symptoms, leading to an increase in depression among the population in Chile. The effect varies by gender and age, with a stronger influence on men and young adults.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Nick Graetz, Carl Gershenson, Sonya R. Porter, Danielle H. Sandler, Emily Lemmerman, Matthew Desmond
Summary: Investments in stable, affordable housing may be an important tool for improving population health. This study, using administrative data, found that high rent burden, increases in rent burden during midlife, and evictions were associated with increased mortality.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Wan Wei
Summary: This study explores the phenomenon of other patient participation in Traditional Chinese Medicine (TCM), uncovering the various roles that third parties can assume during medical interactions. The findings contribute to existing research on patient resistance and triadic medical interactions, providing insights into the dynamics and implications of third-party involvement in medical consultations.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Harry Scarbrough, Katie Rose M. Sanfilippo, Alexandra Ziemann, Charitini Stavropoulou
Summary: This paper examines the contribution of pilot implementation studies to the wider spread and sustainability of innovation in healthcare systems. Through an empirical examination of an innovation intermediary organization in the English NHS, the study finds that their work in mobilizing pilot-based evidence involves configuring to context, transitioning evidence, and managing the transition. The findings contribute to theory by showing how intermediary roles can support the effective transitioning of pilot-based evidence, leading to more widespread adoption and sustainability of innovation.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Marta Seiz, Leire Salazar, Tatiana Eremenko
Summary: This study examines the impact of maternal educational selection on birth outcomes during an economic recession, and finds that more educated mothers are more likely to give birth during high unemployment periods. Additionally, maternal education mitigates the adverse effects of unemployment on birth outcomes and is consistently associated with better perinatal health.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Jingyuan Shi, Hye Kyung Kim, Charles T. Salmon, Edson C. Tandoc Jr, Zhang Hao Goh
Summary: This study examines the influence of individual and collective norms on COVID-19 vaccination intention across eight Asian countries. The findings reveal nuanced patterns of how individual and collective social norms influence health behavioral decisions, depending on the degree of cultural tightness-looseness.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Elliot Friedman, Melissa Franks, Elizabeth Teas, Patricia A. Thomas
Summary: This study found that positive relations with others have a significant impact on functional limitations and longevity in aging adults, independent of social integration and social support.
SOCIAL SCIENCE & MEDICINE
(2024)
Article
Public, Environmental & Occupational Health
Zhuolin Pan, Yuqi Liu, Ye Liu, Ziwen Huo, Wenchao Han
Summary: This study examines the effects of age-friendly neighbourhood environment and functional abilities on life satisfaction among older adults in urban China. The findings highlight the importance of transportation, housing, and social and physical environment factors in influencing functional abilities and life satisfaction. The study provides valuable insights for policymakers in enhancing older adults' life satisfaction in the Chinese urban context.
SOCIAL SCIENCE & MEDICINE
(2024)