A Hybrid K-Harmonic Means with ABC Clustering Algorithm Using an Optimal K Value for High Performance Clustering

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

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Simplification algorithm of denture point cloud based on feature preserving

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215541 ◽

2021 ◽

pp. 1-14

Author(s):

Shigang Wang ◽

Shuai Peng ◽

Jiawen He

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Characteristic Point ◽

Cloud Model ◽

Average Distance ◽

Normal Vector ◽

Sampling Point ◽

Characteristic Region ◽

K Value ◽

Feature Preserving

Due to the point cloud of oral scan denture has a large amount of data and redundant points. A point cloud simplification algorithm based on feature preserving is proposed to solve the problem that the feature preserving is incomplete when processing point cloud data and cavities occur in relatively flat regions. Firstly, the algorithm uses kd-tree to construct the point cloud spatial topological to search the k-Neighborhood of the sampling point. On the basis of that to calculate the curvature of each point, the angle between the normal vector, the distance from the point to the neighborhood centroid, as well as the standard deviation and the average distance from the point to the neighborhood on this basis, therefore, the detailed features of point cloud can be extracted by multi-feature extraction and threshold determination. For the non-characteristic region, the non-characteristic point cloud is spatially divided through Octree to obtain the K-value of K-means clustering algorithm and the initial clustering center point. The simplified results of non-characteristic regions are obtained after further subdivision. Finally, the extracted detail features and the reduced result of non-featured region will be merged to obtain the final simplification result. The experimental results show that the algorithm can retain the characteristic information of point cloud model better, and effectively avoid the phenomenon of holes in the simplification process. The simplified results have better smoothness, simplicity and precision, and are of high practical value.

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A categorization tool for fabric systems used in firefighters' clothing based on their thermal protective and thermo-physiological comfort performances

Textile Research Journal ◽

10.1177/0040517518809055 ◽

2018 ◽

Vol 89 (16) ◽

pp. 3244-3259 ◽

Cited By ~ 2

Author(s):

Sumit Mandal ◽

Simon Annaheim ◽

Andre Capt ◽

Jemma Greve ◽

Martin Camenzind ◽

...

Keyword(s):

Performance Index ◽

Protective Clothing ◽

High Performance ◽

Clustering Algorithm ◽

Health And Safety ◽

Thermal Protective Performance ◽

Thermal Protective Clothing ◽

Low Performance ◽

Fabric System ◽

Protective Performance

Fabric systems used in firefighters' thermal protective clothing should offer optimal thermal protective and thermo-physiological comfort performances. However, fabric systems that have very high thermal protective performance have very low thermo-physiological comfort performance. As these performances are inversely related, a categorization tool based on these two performances can help to find the best balance between them. Thus, this study is aimed at developing a tool for categorizing fabric systems used in protective clothing. For this, a set of commercially available fabric systems were evaluated and categorized. The thermal protective and thermo-physiological comfort performances were measured by standard tests and indexed into a normalized scale between 0 (low performance) and 1 (high performance). The indices dataset was first divided into three clusters by using the k-means algorithm. Here, each cluster had a centroid representing a typical Thermal Protective Performance Index (TPPI) value and a typical Thermo-physiological Comfort Performance Index (TCPI) value. By using the ISO 11612:2015 and EN 469:2014 guidelines related to the TPPI requirements, the clustered fabric systems were divided into two groups: Group 1 (high thermal protective performance-based fabric systems) and Group 2 (low thermal protective performance-based fabric systems). The fabric systems in each of these TPPI groups were further categorized based on the typical TCPI values obtained from the k-means clustering algorithm. In this study, these categorized fabric systems showed either high or low thermal protective performance with low, medium, or high thermo-physiological comfort performance. Finally, a tool for using these categorized fabric systems was prepared and presented graphically. The allocations of the fabric systems within the categorization tool have been verified based on their properties (e.g., thermal resistance, weight, evaporative resistance) and construction parameters (e.g., woven, nonwoven, layers), which significantly affect the performance. In this way, we identified key characteristics among the categorized fabric systems which can be used to upgrade or develop high-performance fabric systems. Overall, the categorization tool developed in this study could help clothing manufacturers or textile engineers select and/or develop appropriate fabric systems with maximum thermal protective performance and thermo-physiological comfort performance. Thermal protective clothing manufactured using this type of newly developed fabric system could provide better occupational health and safety for firefighters.

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AN EFFICIENT CLUSTERING METHOD FOR DBSCAN GEOGRAPHIC SPATIO-TEMPORAL LARGE DATA WITH IMPROVED PARAMETER OPTIMIZATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-581-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 581-584

Author(s):

J. W. Li ◽

X. Q. Han ◽

J. W. Jiang ◽

Y. Hu ◽

L. Liu

Keyword(s):

Parameter Optimization ◽

Clustering Algorithm ◽

Optimal Solution ◽

Large Data ◽

Parameter Selection ◽

Physical Analysis ◽

Clustering Method ◽

K Value ◽

Dbscan Clustering ◽

Spatio Temporal

Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.

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Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1002.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 9-13

Keyword(s):

Cluster Analysis ◽

Precision Agriculture ◽

Crop Production ◽

Clustering Algorithm ◽

Research Work ◽

K Value ◽

Clustering Technique ◽

Production Analysis ◽

Initial Cluster ◽

The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

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