Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP...Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.展开更多
Artificial intelligence(AI)is based on complex artificial neural networks,characterized by layered network architecture,parallel processing of large data sets and iterative algorithms for processing large data sets.AI...Artificial intelligence(AI)is based on complex artificial neural networks,characterized by layered network architecture,parallel processing of large data sets and iterative algorithms for processing large data sets.AI-assisted screening studies have demonstrated non-inferior diagnostic performance,reduced human workload by up to 70%,and reduced recall rates by 25% compared to human double reading.Natural language models promise high accuracy in advising on breast cancer prevention(80%),guiding tumor boards for personalized treatment decisions(50-70%),and planning oncoplastic or radiotherapy treatment for standard cases(72%),but AI sometimes produces errors and fails in complex cases.The main technical advantage of AI is that it can perform routine tasks faster and with fewer errors than humans.This is relevant for scheduling,summarizing reports,recording services for billing and quality assurance.The main concerns in healthcare are the quality of training data,the stability of AI systems,cybersecurity,liability and transparency.Currently,human experts still outperform AI in most areas.AI self-correcting algorithms and the alignment of AI constructed goals with human ethics are imperative to prevent patient harm.The ability of AI to uncover hidden patterns in multi-omics,immune regulation and tumor defense,as well as to develop new drugs,will advance the integrative fight against breast cancer.展开更多
基金Projects(60903082,60975042)supported by the National Natural Science Foundation of ChinaProject(20070217043)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.
文摘Artificial intelligence(AI)is based on complex artificial neural networks,characterized by layered network architecture,parallel processing of large data sets and iterative algorithms for processing large data sets.AI-assisted screening studies have demonstrated non-inferior diagnostic performance,reduced human workload by up to 70%,and reduced recall rates by 25% compared to human double reading.Natural language models promise high accuracy in advising on breast cancer prevention(80%),guiding tumor boards for personalized treatment decisions(50-70%),and planning oncoplastic or radiotherapy treatment for standard cases(72%),but AI sometimes produces errors and fails in complex cases.The main technical advantage of AI is that it can perform routine tasks faster and with fewer errors than humans.This is relevant for scheduling,summarizing reports,recording services for billing and quality assurance.The main concerns in healthcare are the quality of training data,the stability of AI systems,cybersecurity,liability and transparency.Currently,human experts still outperform AI in most areas.AI self-correcting algorithms and the alignment of AI constructed goals with human ethics are imperative to prevent patient harm.The ability of AI to uncover hidden patterns in multi-omics,immune regulation and tumor defense,as well as to develop new drugs,will advance the integrative fight against breast cancer.
