Improvement in k-Means Clustering Algorithm Using Data Clustering

2015 ◽  
Author(s):  
K. Rajeswari ◽  
Omkar Acharya ◽  
Mayur Sharma ◽  
Mahesh Kopnar ◽  
Kiran Karandikar
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Using Data

scholarly journals Algorithm to forming a rule base for a fuzzy classifier designed on the basis of the K-means clustering algorithm and the whale optimization algorithm

2021 ◽  
Vol 24 (1) ◽  
pp. 42-47
Author(s):  
N. P. Koryshev ◽  
◽  
I. A. Hodashinsky ◽  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Performance Testing ◽  
Real Data ◽  
Rule Base ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Whale Optimization ◽  
Clustering Quality ◽  
Using Data

The article presents a description of the algorithm for generating fuzzy rules for a fuzzy classifier using data clustering, metaheuristic, and the clustering quality index, as well as the results of performance testing on real data sets.

An Effective Streams Clustering Method for Biomedical Signals

2010 ◽  
Vol 1 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Pimwadee Chaovalit
Keyword(s):  
Data Streams ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Healthcare Professionals ◽  
Linear Time ◽  
Monitoring Systems ◽  
Clustering Method ◽  
Biomedical Signals ◽  
Using Data ◽  
Aided Diagnosis

In the healthcare industry, the ability to monitor patients via biomedical signals assists healthcare professionals in detecting early signs of conditions such as blocked arteries and abnormal heart rhythms. Using data clustering, it is possible to interpret these signals to look for patterns that may indicate emerging or developing conditions. This can be accomplished by basing monitoring systems on a fast clustering algorithm that processes fast-paced streams of raw data effectively. This paper presents a clustering method, POD-Clus, which can be useful in computer-aided diagnosis. The proposed method clusters data streams in linear time and outperforms a competing algorithm in capturing changes of clusters in data streams.

Analysis of Covid-19 data in India using Data Clustering Algorithm

2020 ◽  
Vol 33 (03) ◽  
Author(s):  
Sumitha T ◽  
◽  
Arun Manicka Raja M ◽  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Using Data

scholarly journals An Efficient Social Spider Optimization for Data Clustering using Data Vector Representation

2020 ◽  
Vol 8 (6) ◽  
pp. 2553-2557
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Vector Representation ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Data Vector ◽  
Using Data ◽  
Nature Inspired Algorithms ◽  
Better Than

In this article, we propose a new clustering algorithm namely an efficient social spider optimization for data clustering using data vector representation (ESSODCDI). It uses a data vector representation for each spider so that its memory requirements can be reduced. Unlike other nature-inspired algorithms, it requires lesser memory requirements. We find that its clustering results are by far better than those of other nature-inspired algorithms.

Balanced Data Clustering Algorithm for Both Hard and Soft Clustering

2018 ◽  
Vol 6 (2) ◽  
pp. 176-183
Author(s):  
Purnendu Das ◽  
◽  
Bishwa Ranjan Roy ◽  
Saptarshi Paul ◽  
◽  
...  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Soft Clustering

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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