scholarly journals Refined Mode-Clustering via the Gradient of Slope

Stats ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 486-508
Author(s):  
Kunhui Zhang ◽  
Yen-Chi Chen
Keyword(s):  
Conventional Method ◽  
Clustering Method ◽  
Computational Theory ◽  
Underlying Distribution ◽  
Sample Test ◽  
Method Show

In this paper, we propose a new clustering method inspired by mode-clustering that not only finds clusters, but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We also design a local two-sample test based on the clustering result that has more power than a conventional method. We apply our method to the Astronomy and GvHD data and show that our method finds meaningful clusters. We also derive the statistical and computational theory of our method.

Fuzzy Clustering Method for Spherical Data Based on q-Divergence

2019 ◽  
Vol 23 (3) ◽  
pp. 561-570 ◽  
Author(s):  
Masayuki Higashi ◽  
◽  
Tadafumi Kondo ◽  
Yuchi Kanzawa
Keyword(s):  
Conventional Method ◽  
Fuzzy Clustering ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Kl Divergence ◽  
Spherical Data ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy Clustering Method ◽  
Vectorial Data

This study presents a fuzzy clustering algorithm for classifying spherical data based on q-divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q-divergence. Next, based on the knowledge that q-divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q-divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q-divergence. Finally, some numerical experiments are conducted to verify the proposed methods.

Incremental Hierarchical Clustering for Data Insertion and Its Evaluation

2020 ◽  
Vol 8 (2) ◽  
pp. 1-22
Author(s):  
Kakeru Narita ◽  
Teruhisa Hochin ◽  
Yoshihiro Hayashi ◽  
Hiroki Nomiya
Keyword(s):  
Hierarchical Clustering ◽  
Conventional Method ◽  
Execution Time ◽  
Experimental Results ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Data Points ◽  
Entire Dataset

Clustering is employed in various fields. However, the conventional method does not consider changing data. Therefore, if the data is changed, the entire dataset must be re-clustered. This article proposes a clustering method to update the clustering result obtained by a hierarchical clustering method without re-clustering when a point is inserted. This article defines the center and the radius of a cluster and determine the cluster to be inserted. The insertion location is determined by similarity based on the conventional clustering method. this research introduces the concept of outliers and consider creating a cluster caused by the insertion. By examining the multimodality of a cluster, the cluster is divided. In addition, when the number of clusters increases, data points previously inserted are updated by re-insertion. Compared with the conventional method, the experimental results demonstrate that the execution time of the proposed method is significantly smaller and clustering accuracy is comparable for some data.

A Computational Theory of Language

1989 ◽  
Vol 34 (7) ◽  
pp. 686-687
Author(s):  
Stephen G. Pulman
Keyword(s):  
Computational Theory ◽  
Theory Of Language

THE EFFECT OF APPLYING SQ3R METHOD ON STUDENTS’ ACHIEVEMENT IN READING COMPREHENSION

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Pranata Royganda Sihaloho And Masitowarni Siregar
Keyword(s):  
Reading Comprehension ◽  
Research Design ◽  
Conventional Method ◽  
Experimental Research ◽  
T Test ◽  
Control Group ◽  
Multiple Choice Tests ◽  
Choice Tests ◽  
And Control ◽  
Experimental Group

The aim of this study is to discover the effect of applying SQ3R method inreading comprehension. Experimental research design is used as the research method.This research took place at SMA Nasrani 2Medan. There were 2 classes chosen as thesample with 30 students in each class. The classes were divided into two groups namelyexperimental and control group. The experimental group was taught by using SQ3Rmethod and the control group was taught by using conventional method. The instrumentused to collect the data was a set of multiple choice tests, which divided as pre test andpost test. The result of the research was analyzed by using t-test formula. The resultshowed that t-test was higher than t-table (4,23>2,00) at the level of significant 0,05with degree of freedom (df) 58. It means that hypothesis alternative (Ha) is acceptedwhich shows that SQ3R method significantly improves the student’s readingcomprehension.

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