An evolutionary many-objective approach to multiview clustering using feature and relational data

To improve the boundary processing ability and anti-noise performance of image segmentation algorithm,a neutrosophic fuzzy clustering algorithm based on non-local information is proposed here. Initially, the proposed approach uses the data distribution of deterministic subset to determine the clustering centre of the fuzzy subset. Besides, the fuzzy non-local pixel correlation is introduced into the neutrosophic fuzzy mean clustering algorithm. The experimental results on synthetic images, medical images and natural images show that the proposed method is more robust and more accurate than the existing clustering segmentation methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5949

Image segmentation algorithm based on neutrosophic fuzzy clustering with non-local information

Clustering is a classic combined optimization problem that is widely used in pattern recognition, image processing, market analysis and so on. However, the efficiency of clustering algorithms decreases as the amount of data increases. In addition, most of the existing methods optimize only one objective and therefore may be suitable only for datasets with certain features. To address these limitations, in this paper, we develop a new hybrid chain-hypergraph P system (named HCHPS), which makes full use of the parallelism of P systems as well as the advantages of chain and hypergraph topology structures for accurate and efficient clustering. Our new P system comprises three types of subsystems, i.e., reaction chain membrane subsystems, local communication membrane subsystems and global ensemble membrane subsystems. Each type of subsystems is implemented end-to-end in HCHPS with new rules and membrane structures in parallel. In particular, to obtain efficient clustering center objects and make the algorithm robust to data with various features, the reaction chain membrane subsystems perform three different multiobjective strategies simultaneously by new chain evolution rules. To increase the population diversity of cluster centers, the local communication membrane subsystems utilize transport rules between membranes for coevolution of nondominated objects. The global ensemble membrane subsystems conduct a new dense representation multisize ensemble strategy to further improve the accuracy of the final results. Evaluations on two artificial data sets and 17 real-life data sets demonstrate the robustness of the proposed method in correctly clustering data sets with different dimensions and shapes. Our experimental results outperform those of both baseline and state-of-the-art methods. Moreover, benefiting from the parallelism, HCHPS is less time consuming than other methods, featuring an average completion time of 28.07 seconds on the 17 real-life data sets. Moreover, an ablation study shows that our proposed components are critical for effective cluster analysis.

Hybrid Chain-Hypergraph P Systems for Multiobjective Ensemble Clustering

Progress in the development and application of mechanized rice ratooning technology in Guangdong

Multi-view problems generalize standard machine learning problems to situations in which data entities are described from multiple different perspectives, a situation that arises in many applications due to the consideration of multiple data sources or multiple metrics of dissimilarity between entities. Multi-view algorithms for data clustering offer the opportunity to fully consider and integrate this information during the clustering process, but current algorithms are often limited to the use of two views. Here, we describe the design of an evolutionary algorithm for the problem of multi-view data clustering. The use of a many-objective evolutionary algorithm addresses limitations of previous work, as the resulting method should be capable of scaling to settings with four or more views. We evaluate the performance of our proposed algorithm for a set of traditional benchmark datasets, where multiple views are derived using distinct measures of dissimilarity. Our results demonstrate the ability of our method to effectively deal with a many-view setting, as well as the performance boost obtained from the integration of complementary measures of dissimilarity for both synthetic and real-world datasets.

Many-view clustering: an illustration using multiple dissimilarity measures

Alternate wetting and drying irrigation is a widely used irrigation method for water-savings and high-yielding in rice (Oryza sativa L.) production; however, its effect on grain yield (GY) in the main and ratoon crops is unclear. With two rice cultivars as materials, the effects on GY in the main and ratoon crop of three irrigation regimes, that is, conventional flooding irrigation (CI), alternate wetting and moderate soil drying irrigation (WMD), and alternate wetting and severe soil drying irrigation (WSD), during grain filling of the main crop were investigated. Compared with CI, WMD increased GY by 6.0∼6.5% in the main crop by increasing the 1,000-grain weight and filled grain rate, whereas the ratoon crop GY increased by 13.3∼14.6% via increased panicle number. The WSD had no significant effects on the main crop GY, but significantly decreased ratoon crop GY by decreasing panicle number. A higher leaf photosynthetic rate, leaf area index, root oxidation activity, dry matter accumulation and nonstructural carbohydrate (NSC) translocation under WMD primarily drove the increased 1,000-grain weight and filled grain rate in the main crop. Under WMD, higher soluble sugar contents catalysed by high amylase activity and higher zeatin + zeatin riboside in stems of the main crop promoted the growth of regenerated buds and increased the ratoon crop panicle number. In conclusion, WMD during grain filling of the main crop improved the growth and development of the main crop and promote the germination of regenerated buds, thereby increasing the GY of both main and ratoon crops. 

Alternate wetting and moderate soil drying could increase grain yields in both main and ratoon rice crops

Most of the existing clustering algorithms are often based on Euclidean distance measure. However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures. Thus, it is necessary to combine multiple distance measures into clustering. However, the weights for different distance measures are hard to set. Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique. Recently a new clustering algorithm called ‘multiobjective evolutionary clustering based on combining multiple distance measures’ (MOECDM) was proposed to integrate Euclidean and Path distance measures together for partitioning the dataset with different structures. However, it is time-consuming due to the large-sized genes. This paper proposes a fast multiobjective fuzzy clustering algorithm for partitioning the dataset with different structures. In this algorithm, a real encoding scheme is adopted to represent the individual. Two fuzzy clustering objective functions are designed based on Euclidean and Path distance measures, respectively, to evaluate the goodness of each individual. An improved evolutionary operator is also introduced accordingly to increase the convergence speed and the diversity of the population. In the final generation, a set of nondominated solutions can be obtained. The best solution and the best distance measure are selected by using a semisupervised method. Afterwards, an updated algorithm is also designed to detect the optimal cluster number automatically. The proposed algorithms are applied to many datasets with different structures, and the results of eight artificial and six real-life datasets are shown in experiments. Experimental results have shown that the proposed algorithms can not only successfully partition the dataset with different structures, but also reduce the computational cost.

/pdf/a-fast-multiobjective-fuzzy-clustering-with-multimeasures-3fp5gnvrqh.pdf

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

We consider a directed polymer model in dimension 1+1, where the random walk is attracted to stable law and the environment is independent in time variable and correlated in space variable. We obtain the scaling limit in the intermediate disorder regime for partition function, and show that the rescaled point-to-point partition function of directed polymers converges in the space of continuous functions to the solution of a stochastic heat equation driven by time-white spatial-colored noise. The scaling limit of the polymer transition probability is also established in the path space. The proof of the tightness is based on the gradient estimates for symmetric random walks in the domain of normal attraction of α-stable law which are established in this paper. 

https://projecteuclid.org/journals/electronic-journal-of-probability/volume-28/issue-none/Scaling-limits-of-directed-polymers-in-spatial-correlated-environment/10.1214/23-EJP955.pdf

Scaling limits of directed polymers in spatial-correlated environment

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yingxia Chen

Author Tools

Chat about Author

Papers

Alternate wetting and moderate soil drying could increase grain yields in both main and ratoon rice crops

A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

Scaling limits of directed polymers in spatial-correlated environment

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy