Fuzzy K-means Clustering Algorithm Research Articles

Retracted: Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm.

[This retracts the article DOI: 10.1155/2022/4278524.].

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm.

Learning about the regional business model is essential for the sustainable development of the regional economy. From the perspective of urban renewable energy, city A is the product of energy development. This paper analyzes the current situation and existing problems of the industrial model of city A through fuzzy k-means clustering algorithm. The results show that although the optimization of industrial structure in city A has achieved some results, the more intuitive problems mainly include low labor productivity of the primary industry, strong resource dependence, insufficient extension of industrial chain, and slow development of technology intensive industries. This paper uses fuzzy k-means clustering algorithm to select the leading industries from the perspective of the current situation of leading industries, urban development pattern, and regional policies in city A. The results show that, as a renewable resource-based city, the leading industries suitable for the current development of city A include manufacturing, power, alkali gas and water production and supply, transportation, warehousing and postal industry, leasing, and business services. The results of fuzzy k-means clustering algorithm are quite excellent, and the accuracy rate is 93.3%. This paper uses the grey dynamic linear programming model to predict the future development of the Urban A business model and combines the selection of key functions to obtain the best business model: deep and efficient technical equipment as a good goal, achieved through regional logistics, transportation, new services, etc., to enhance the output value of the tertiary industry in city A and optimize the internal structure of the secondary industry in city A.

English Teaching Ability Evaluation Based on Fuzzy K-means Clustering Algorithm

With the continuous progress of my country’s social economy and the continuous improvement of people’s material living standards, people’s demand for teaching standards is increasing. In recent years, many higher vocational colleges in our country have hired many foreign teachers when opening English classes. Higher vocational colleges are different from traditional schools. They pay particular attention to cultivating students’ skills. Using foreign teachers in English classrooms can enable students to understand English knowledge and improve their later use ability. However, in English classroom teaching, too many foreign teachers have brought a lot of problems to the teaching ability evaluation system of English foreign teachers and caused difficulties to the teaching management of higher vocational colleges. Based on the fuzzy K-means clustering algorithm, this paper has launched a series of research and discussion on the evaluation of English teaching ability. In the context of the modern big data era, in order to improve the teaching ability of English teachers, improve the quality of college teachers, and provide students with better teaching services, teachers can start with the main factors that influence and influence the improvement of college English teaching ability. In this paper, by designing a college English teaching ability evaluation system, based on the establishment of the constraint parameter index analysis model, the ability evaluation of the big data information model is carried out, and the ability constraint function information is extracted. A combination of K-means clustering algorithms, clustering and integrating proficiency index parameters, completing the preparation of an educational resource allocation plan, and achieving scientific evaluation of the English language ability. The experimental results show that the use of the K-means fuzzy clustering algorithm to evaluate the English language proficiency has an excellent information fusion analysis capability. This improves the accuracy of educational ability evaluation and the efficiency of educational resources.

On the Measures of Separation of a Fuzzy Clustering

This paper tested the measures of separation of a fuzzy clustering. Over the same labeled data, Fuzzy k-Means clustering algorithm generates the first fuzzy clustering, then the proposed revision function in (6) revises it several times to generate various fuzzy partitions with different pattern recognition rates computed by (5), finally the measures of separation measure the separation of each fuzzy clustering. Experimental results on real data show that the measures of separation in literatures fail to measure the separation of a fuzzy clustering in some cases, for they argue that the fuzzy clustering with higher pattern recognition rate is less separate between clusters and worse than that with lower pattern recognition rate.

On the Measure of Compactness of Fuzzy Clustering

This paper tested the measures of compactness of fuzzy partitions. Over the same labeled data, Fuzzy k-Means clustering algorithm generates the first partition, then the proposed revision function in (7) revises it several times to generate various fuzzy partitions with different pattern recognition rates computed by (6), finally the measures of compactness measure the compactness of each fuzzy partition. Experimental results on real data show that the measures of compactness in literatures fail to measure the compactness of a fuzzy clustering in some cases, for they argue that the fuzzy clustering with higher pattern recognition rate is less compact and worse than that with lower pattern recognition rate.

Identifying common near-surface and subsurface stratigraphic units using EM34 signal data and fuzzy k-means analysis in the Darling River valley

In irrigated areas of central and northern New South Wales secondary soil salinisation is of increasing concern. This is particularly the case for the Bourke Irrigation District (mid-Darling River valley) where incipient traces of point-source salinisation are evident. Much of this is attributed to significant changes in the water balance and in particular increased deep drainage and mobilisation of primary salts. In the first instance, near-surface prior-stream channels and subsurface paleochannels evident within Cenozoic alluvial deposits act as significant hydrological features. Owing to the porosity and connectivity of the Cenozoic sediments, deep draining water interacts with ancient and highly saline Cretaceous marine mudstones. Unfortunately, natural resource data on the spatial distribution of near-surface and subsurface stratigraphy are not readily available to understand these processes or determine best management. In this paper, we demonstrate how fuzzy k-means (FKM) analysis of EM34 signal data collected across the Bourke Irrigation District can be used to identify and map common near-surface and subsurface stratigraphic units. EM34 survey was carried out in the horizontal mode of operation with measurements made at coil spacing of 10, 20 and 40 m (i.e. EM34-10, -20 and -40, respectively). In all, 1236 sites were visited on a ∼0.5–1 km grid. The EM34 signal data were classified numerically using the FKM clustering algorithm. The iteration of fuzzy exponents (f) and various indices, including the fuzziness performance index (FPI) and normalised classification entropy (NCE) enabled the determination of f = 1.6 and k = 4 classes to be selected for further investigation. Using fuzzy canonical analysis we find that the EM34-10 and EM34-20 signal data best discriminate the classes. The resulting k = 4 classes (i.e. Class A, B, C and D) are then mapped using a method that ensures summation of class membership (m) values to unity and using local ordinary kriging. Class A represents saline and clay-rich sediments associated with outcrops of the Cretaceous marine mudstone. Class D represents interbedding of sand within the clay units indicative of the Cenozoic alluvial deposits that overly the Cretaceous mudstone. Similarly, Class B represents Cenozoic sediments with the presence of paleochannel(s). Class C characterises the eolian dune and alluvial floodplain physiographic units. The use of a confusion index (CI) highlights areas of uncertainty in the FKM classification mapping and indicates where the collection of additional information is appropriate. Validation of the FKM approach is confirmed by calculating near-surface (0–6 m) and subsurface (6–12 m) class average soil physical and chemical property (i.e. clay content, cation exchange capacity and salinity) variance (i.e. S2 Z ) and total within-class variance (i.e. S 2 T ) of a FKM map. Overall, the average subsurface ECe data produces the greatest reduction in variance. This suggests the FKM classification of EM34 signal data is influenced by saline groundwater-tables in the Cenozoic sediments which lie above the Cretaceous marine mudstone. The FKM class memberships and CI along a short but detailed transect across the Bourke Irrigation District are interpreted with respect to the spatial distribution of near-surface and subsurface soil property data. We conclude the FKM analysis of EM34 provides a better understanding of the spatial distribution and horizontal layering of the various stratigraphic units which characterise the Bourke Irrigation District. The results help explain the broad hydrological processes driving point source secondary soil salinisation and provides a framework for the deployment of measuring and monitoring sites.

Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters

In this paper, we present an agglomerative fuzzy K-means clustering algorithm for numerical data, an extension to the standard fuzzy K-means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters in a data set, which is a well known problem in $k$-means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20000 objects and 100 clusters, and the WINE data set of 178 objects, 17 dimensions and 3 clusters from UCI, have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters.