Research on Distance Judgement Method from Point to Line to Generate a Line

Regarding the problem that the traditional straight-line generating has a low accuracy, we study straightline generating with the distance of point to line. We explore generating a line to approximate the ideal line and the issue is to pick out the pixel point of approximating the ideal line. The paper plays a significant scientific role in elucidating linear optimization norm and it lays a foundation for showing a straight line. The algorithm is valuable for computer graphics.

Bat-Grey Wolf Optimizer and kernel mapping for automatic incremental clustering

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails,internet and web pages.Therefore,it becomes a complex task for arranging and browsing the required document.This paper proposes an approach for incremental clustering using the BatGrey Wolf Optimizer(BAGWO).The input documents are initially subjected to the pre-processing module to obtain useful keywords,and then the feature extraction is performed based on wordnet features.After feature extraction,feature selection is carried out using entropy function.Subsequently,the clustering is done using the proposed BAGWO algorithm.The BAGWO algorithm is designed by integrating the Bat Algorithm(BA)and Grey Wolf Optimizer(GWO)for generating the different clusters of text documents.Hence,the clustering is determined using the BAGWO algorithm,yielding the group of clusters.On the other side,upon the arrival of a new document,the same steps of pre-processing and feature extraction are performed.Based on the features of the test document,the mapping is done between the features of the test document,and the clusters obtained by the proposed BAGWO approach.The mapping is performed using the kernel-based deep point distance and once the mapping terminated,the representatives are updated based on the fuzzy-based representative update.The performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy,Jaccard coefficient,and rand coefficient with maximal values 0.948,0.968,and 0.969,respectively.

ParSymG: a parallel clustering approach for unsupervised classification of remotely sensed imagery

Symmetry is a common feature in the real world.It may be used to improve a classification by using the point symmetry-based distance as a measure of clustering.However,it is time consuming to calculate the point symmetry-based distance.Although an efficient parallel point symmetry-based K-means algorithm(ParSym)has been propsed to overcome this limitation,ParSym may get stuck in sub-optimal solutions due to the K-means technique it used.In this study,we proposed a novel parallel point symmetry-based genetic clustering(ParSymG)algorithm for unsupervised classification.The genetic algorithm was introduced to overcome the sub-optimization problem caused by inappropriate selection of initial centroids in ParSym.A message passing interface(MPI)was used to implement the distributed master–slave paradigm.To make the algorithm more time-efficient,a three-phase speedup strategy was adopted for population initialization,image partition,and kd-tree structure-based nearest neighbor searching.The advantages of ParSymG over existing ParSym and parallel K-means(PKM)alogithms were demonstrated through case studies using three different types of remotely sensed images.Results in speedup and time gain proved the excellent scalability of the ParSymG algorithm.