scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

scholarly journals A Meta-heuristic Density-Based Subspace Clustering Algorithm for High Dimensional Data

2021 ◽  
Author(s):  
Parul Agarwal ◽  
Shikha Mehta ◽  
Ajith Abraham
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Rank Test ◽  
Test Results ◽  
Dbscan Algorithm ◽  
Signed Rank ◽  
Subspace Algorithms ◽  
Signed Rank Test

Abstract Subspace clustering is one of the efficient techniques for determining the clusters in different subsets of dimensions. Ideally, these techniques should find all possible non-redundant clusters in which the data point participates. Unfortunately, existing hard subspace clustering algorithms fail to satisfy this property. Additionally, with the increase in dimensions of data, classical subspace algorithms become inefficient. This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters of the DBCAN algorithm through a hybrid meta-heuristic algorithm and uses hashing concepts to discover all non-redundant subspace clusters. The efficacy of S_FAD is evaluated against various existing subspace clustering algorithms on artificial and real datasets in terms of F_Score and rand_index. Performance is assessed based on three parameters: average ranking, SRR ranking, and scalability on varied dimensions. Statistical analysis is performed through the Wilcoxon signed-rank test. Results reveal that S_FAD performs considerably better on the majority of the datasets and scales well up to 6400 dimensions on the actual dataset.

Research on Spectral Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1350-1353
Author(s):  
Li Li Fu ◽  
Yong Li Liu ◽  
Li Jing Hao
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Theoretical Foundation ◽  
Clustering Algorithms ◽  
Spectral Graph Theory ◽  
Graph Partition ◽  
Mining Areas ◽  
Spectral Graph ◽  
Definition Of ◽  
Spectral Clustering Algorithm

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.

scholarly journals An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Shuxia Ren ◽  
Shubo Zhang ◽  
Tao Wu
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Transition Probability ◽  
Clustering Algorithms ◽  
Detection Algorithm ◽  
Community Information ◽  
The Mean ◽  
Community Detection Algorithm ◽  
Spectral Clustering Algorithm

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

scholarly journals A Spectral Clustering Algorithm Improved by P Systems

2018 ◽  
Vol 13 (5) ◽  
pp. 759-771 ◽  
Author(s):  
Guangchun Chen ◽  
Juan Hu ◽  
Hong Peng ◽  
Jun Wang ◽  
Xiangnian Huang
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Membrane Computing ◽  
Clustering Algorithms ◽  
P System ◽  
Comparison Results ◽  
Clustering Effect ◽  
Computing Framework ◽  
Spectral Clustering Algorithm ◽  
Selection Of

Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. Under the control of evolutioncommunication mechanism, the tissue-like P system can obtain a good clustering partition for each dataset. The proposed spectral clustering algorithm is evaluated on three artiffcial datasets and ten UCI datasets, and it is further compared with classical spectral clustering algorithms. The comparison results demonstrate the advantage of the proposed spectral clustering algorithm.

scholarly journals Exploratory Item Classification Via Spectral Graph Clustering

2017 ◽  
Vol 41 (8) ◽  
pp. 579-599 ◽  
Author(s):  
Yunxiao Chen ◽  
Xiaoou Li ◽  
Jingchen Liu ◽  
Gongjun Xu ◽  
Zhiliang Ying
Keyword(s):  
Cluster Analysis ◽  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Latent Class ◽  
Clustering Algorithms ◽  
Eysenck Personality Questionnaire ◽  
Computationally Efficient ◽  
Computational Overhead ◽  
Spectral Clustering Algorithm

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire.

Spectral Clustering Algorithm: MATLAB PCT-Based Parallel Design and Implementation

2013 ◽  
Vol 765-767 ◽  
pp. 580-584
Author(s):  
Yu Yang ◽  
Cheng Gui Zhao
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Parallel Structure ◽  
Computational Time ◽  
Similarity Matrix ◽  
Data Intensive ◽  
Clustering Quality ◽  
Spectral Clustering Algorithm

Spectral clustering algorithms inevitable exist computational time and memory use problems for large-scale spectral clustering, owing to compute-intensive and data-intensive. We analyse the time complexity of constructing similarity matrix, doing eigendecomposition and performing k-means and exploiting SPMD parallel structure supported by MATLAB Parallel Computing Toolbox (PCT) to decrease eigendecomposition computational time. We propose using MATLAB Distributed Computing Server to parallel construct similarity matrix, whilst using t-nearest neighbors approach to reduce memory use. Ultimately, we present clustering time, clustering quality and clustering accuracy in the experiments.

A Spectrum Clustering Algorithm Based on Weighted Fuzzy Similar Matrix

2012 ◽  
Vol 482-484 ◽  
pp. 2109-2113
Author(s):  
Qiang Li
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Entry Point ◽  
Data Set ◽  
Global Optimal ◽  
Clustering Effect ◽  
Spectral Clustering Algorithm

Unlike those traditional clustering algorithms, the spectral clustering algorithm can be applied to non-convex sphere of sample spaces and be converged to global optimal. As a entry point that the similar of spectral clustering, introduce improved weighted fuzzy similar matrix to spectral in this paper which avoids influence from parameters changes of fuzzy similar matrix in traditional spectral clustering on clustering effect and improves the effectiveness of clustering. It is more actual and scientific, which is tested based on UCI data set.

PENGARUH TIRAH BARING TERHADAP PENURUNAN RASA MUAL PADA KLIEN GASTRITIS DI PELAYANAN KESEHATAN

2019 ◽  
Vol 3 (1) ◽  
pp. 25
Author(s):  
Dewi Nurhanifah ◽  
Desy Noor Latifah Sari ◽  
Rahmawati Rahmawati
Keyword(s):  
Rank Test ◽  
Purposive Sampling ◽  
Signed Rank ◽  
Wilcoxon Signed Rank Test ◽  
Signed Rank Test

Salah satu masalah kesehatan yang sering dialami adalah penyakit gastritis. Gejala yang sering dikeluhkan oleh penderita gastritis adalah mual. Salah satu penatalaksanaan keperawatan yang dapat mengurangi rasa mual adalah tirah baring. Penelitian ini bertujuan untuk mengetahui pengaruh tirah baring terhadap penurunan rasa mual pada klien gastritis di Pelayanan Kesehatan. Metode penelitian menggunakan eksperimental dengan bentuk penelitian one group pretest-posttest design. Populasi dan sampel adalah klien yang mengalami mual di Wilayah Kerja Puskesmas  yang berjumlah 15 orang. Sampel diambil dengan teknik purposive sampling. Alat pengumpul data menggunakan observasi. Analisa data melalui uji Wilcoxon Signed Rank Test. Hasil penelitian menujukkan klien gastritis sebelum tirah baring mengalami mual ringan sebanyak 7 orang (46,7%), sesudah tirah baring mengalami tidak mual sebanyak 7 orang (46,7%). Ada pengaruh tirah baring terhadap penurunan rasa mual pada klien gastritis di Pelayanan Kesehatan (ρ value = 0,001).

Efektifitas Kompres Jahe Merah Hangat Dan Kompres Serai Terhadap Penurunan Intensitas Nyeri Arthritis Remathoid Pada Lanjut Usia Di Desa Mojoranu Kecamatan Dander Kabupaten Bojonegoro

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Ferawato Ferawati
Keyword(s):  
Pain Intensity ◽  
Therapy Group ◽  
The Elderly ◽  
Rank Test ◽  
Test Design ◽  
Signed Rank ◽  
Wilcoxon Signed Rank Test ◽  
Post Test ◽  
Signed Rank Test ◽  
Red Ginger

ABSTRAKReumatoid Artritis (RA) merupakan penyakit muscoloskelektal yang sering terjadi pada usia lanjut. Gangguan pada system muscoloskelektal yang ditandai dengan munculnya nyeri sendi dan kekakuan yang mengakibatkan penurunan kemampuan fisiologis atau kualitas hidup lansia. Dampak dari Reumatoid Artritis dapat menimbulkan beberapa keluhan dan dapat menyebabkan kelumpuhan. Untuk menganalisis efektifitas kompres jahe merah hangat dan kompres serai hangat terhadap penurunan intensitas nyeri artitris remauthoid pada lanjut usia.Metode Penelitian: Jenis penelitian adalah quasy experimental dengan two group pre – post test design. Subjek adalah sebagian lansia yang penderita Arthritis Remathoid di Desa Sumberagung Kecamatan Dander Kabupaten Bojonegoro. Subjek dibagi menjadi dua kelompok yaitu kelompok I (n=15) diberi perlakuan kompres jahe hangat dan II (n=15) diberi perlakuan kompres serai hangat. Analisis yang digunakan uji Mann Whitney U Test dan Wilcoxon Signed Ranks Test dengan ingkat kemaknaan α = 0,05.Hasil uji Wilcoxon Signed Rank Test, didapat keduanya mempunyai nilai kemaknaan yaitu ρ value = 0,000. Nilai ρ = 0,031 pada kelompok kompres serai hangat dan kelompok kompres jahe merah ρ value = 0,165. Hasil uji Mann Withney U Test pada Post perlakuan kedua terapi diperoleh selisih nilai nyeri pada kompres jahe ρ= 0,003 dan selisih nilai nyeri kompres serai ρ value = 0,001.Penggunaan kompres jahe merah lebih efektif dibandingkan dengan kompres serai terhadap penurunan intensitas nyeri arthritis remathoid. Kata Kunci: usia lanjut, Reumatoid Artritis (RA), jahe merah, serai, perbedaan efektifitas.    ABSTRACTReumatoid Artritis (RA) is a musculoskeletal which frequently occurs in the elderly. The disorders in the musculoskeletal system are noted by the occurrence of pain in the joints and stiffness which reduces the physiological abilities or life quality of the elderly. The disease causes many such complaints and  consequences of the disease rheumatoid arthritis may experience paralysis. The aims of this study is to analyze the effect of warm red ginger compress therapy and warm lemongrass compress therapy against of  Decreased pain intensity in  the elderly  with  artitris remauthoid. The study was Queasy experimental with two group pre – post test design. Subjects were some elderly people with Arthritis Remathoid in Sumberagung Village, Dander Sub District, Bojonegoro District. Subjects were divided into two groups: group I (n-15) with warm ginger compress therapy, and II (n=15) with warm lemongrass compress therapy. The analyses used in this study were the Mann Whitney U Test and Wilcoxon Signed Ranks Test with α of 0.05. Results of Wilcoxon Signed Rank Test obtained Both have meaning p value of  0.000. ρ value = 0,031 in a warm lemongrass compress therapy group and obtained of warm ginger compress therapy group ρ value = 0,165. The results of Mann Withney U Test on Post treatment second therapy, obtained difference of warm ginger compress therapy with ρ value= 0,003 and difference of warm lemongrass compress therapy with ρ value = 0,001.The use of warm ginger compresses therapy are more effective than a warm lemongrass compress therapy against decreased pain intensity in  the elderly  with  artitris remauthoid.  Keywords: elderly, artitris remauthoid, red ginger, lemongrass, differences in effectiveness

The Degree of Subjective Complaints of Students Practice in Mechanical Technology Laboratories

2018 ◽  
Vol 4 (1) ◽  
pp. 22
Author(s):  
I Ketut Widana
Keyword(s):  
Engineering Students ◽  
Standing Position ◽  
Statistical Test ◽  
Rank Test ◽  
Cross Sectional ◽  
Signed Rank ◽  
Before And After ◽  
Signed Rank Test ◽  
The Difference ◽  
Paired T Test

The working practice of the engineering students is part of the learning process that is irreducible and indispensable. The composition of  lecturing between theoretical and practical one is 40% to 60%. With this condition, the students spend more time at the laboratory. Generally, the students perform in the laboratory work by standing position. The design of research is observational cross-sectional. The method applied is observation, interview and measuring. The subjects of research are practicing students amounting to 21 students. Referring to the analysis of statistical test or Wilcoxon signed ranks test, the difference of effect of work position is significant, namely p < 0.05 towards musculoskeletal disorders (MSDs) before and after working. The quantity of the average complaint after working is score 44.62 ± 9.47. The result of Wilcoxon signed rank test shows that there is significant different effects of standing work position, namely p < 0.05 towards fatigue generally before and after working. The degree of the working pulse is on the average of 110.78  ± 17.80 bpm (beats per minutes) which can be categorized into the medium workload. Using paired t-test, the result is p < 0.05.

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