scholarly journals Research on Clustering Method of Improved Glowworm Algorithm Based on Good-Point Set

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yaping Li ◽  
Zhiwei Ni ◽  
Feifei Jin ◽  
Jingming Li ◽  
Fenggang Li
Keyword(s):  
Clustering Algorithm ◽  
Initial Distribution ◽  
Data Sets ◽  
Clustering Method ◽  
Data Object ◽  
Good Point ◽  
Point Set ◽  
Clustering Optimization ◽  
Basic Ideas ◽  
Good Point Set

As an important data analysis method in data mining, clustering analysis has been researched extensively and in depth. Aiming at the limitation of K-means clustering algorithm that it is sensitive to the distribution of initial clustering center, Glowworm Swarm Optimization (GSO) Algorithm is introduced to solve clustering problems. Firstly, this paper introduces the basic ideas of GSO algorithm, K-means algorithm, and good-point set and analyzes the feasibility of combining them for clustering optimization. Next, it designs a clustering method of improved GSO algorithm based on good-point set which combines GSO algorithm and classical K-means algorithm together, searches data object space, and provides initial clustering centers for K-means algorithm by means of improved GSO algorithm and thus obtains better clustering results. Major improvement of GSO algorithm is to optimize the initial distribution of glowworm swarm by introducing the theory and method of good-point set. Finally, the new clustering algorithm is applied to UCI data sets of different categories and numbers for clustering test. The advantages of the improved clustering algorithm in terms of sum of squared errors (SSE), clustering accuracy, and robustness are explained through comparison and analysis.

Modified K-means clustering algorithm based on good point set and Leader method

2011 ◽  
Vol 31 (5) ◽  
pp. 1359-1362
Author(s):  
Yan-ping ZHANG ◽  
Juan ZHANG ◽  
Cheng-gang HE ◽  
Wei-cui CHU ◽  
Li-na ZHANG
Keyword(s):  
Clustering Algorithm ◽  
Good Point ◽  
Point Set ◽  
Good Point Set

scholarly journals Research on Financial Early-warning based on GIHS Improved BP_AdaBoost Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03004
Author(s):  
Gui-fang Shen ◽  
Yi-Wen Zhang
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Early Warning ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Adaboost Algorithm ◽  
Good Point ◽  
Ensemble Learning Algorithm ◽  
Point Set ◽  
Good Point Set

To improve the accuracy of the financial early warning of the company, aiming at defects of slow learning speed, trapped in local solution and inaccurate operating result of the traditional BP neural network with random initial weights and thresholds, a parallel ensemble learning algorithm based on improved harmony search algorithm using good point set (GIHS) optimize the BP_Adaboost is proposed. Firstly, the good-point set is used to construct a more high quality initial harmony library, and it adjusts the parameters dynamically during the search process and generates several solutions in each iteration so as to make full use of information of harmony memory to improve the global search ability and convergence speed of algorithm. Secondly, ten financial indicators are chosen as the inputs of BP neural network value, and GIHS algorithm and BP neural network are combined to construct the parallel ensemble learning algorithm to optimize BP neural network initial weights value and output threshold value. Finally, many of these weak classifier is composed as strong classifier through the AdaBoost algorithm. The improved algorithm is validated in the company's financial early warning. Simulation results show that the performance of GIHS algorithm is better than the basic HS and IHS algorithm, and the GIHS-BP_AdaBoost classifier has higher classification and prediction accuracy.

On Fuzzy Non-Metric Model for Data with Tolerance and its Application to Incomplete Data Clustering

2016 ◽  
Vol 20 (4) ◽  
pp. 571-579 ◽  
Author(s):  
Yasunori Endo ◽  
◽  
Tomoyuki Suzuki ◽  
Naohiko Kinoshita ◽  
Yukihiro Hamasuna ◽  
...  
Keyword(s):  
Data Clustering ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Uncertain Data ◽  
Data Sets ◽  
Membership Degree ◽  
Clustering Methods ◽  
Clustering Method ◽  
Numerical Examples ◽  
Metric Model

The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some clustering methods were constructed according to this concept, e.g. fuzzyc-means for data with tolerance. These methods can handle uncertain data in the framework of optimization. Thus, in the present study, we apply the concept to FNM. First, we propose a new clustering algorithm based on FNM using the concept of tolerance, which we refer to as the fuzzy non-metric model for data with tolerance. Second, we show that the proposed algorithm can handle incomplete data sets. Third, we verify the effectiveness of the proposed algorithm based on comparisons with conventional methods for incomplete data sets in some numerical examples.

Rough ISODATA Algorithm

2013 ◽  
Vol 3 (4) ◽  
pp. 1-14 ◽  
Author(s):  
S. Sampath ◽  
B. Ramya
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Vital Role ◽  
Data Sets ◽  
Clustering Method ◽  
Data Set ◽  
Number Of Clusters ◽  
Real Life Data ◽  
Nonparametric Statistical

Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering. Even though k-means clustering is a popular clustering method widely used, its application requires the knowledge of the number of clusters present in the given data set. Several solutions are available in literature to overcome this limitation. The k-means clustering method creates a disjoint and exhaustive partition of the data set. However, in some situations one can come across objects that belong to more than one cluster. In this paper, a clustering algorithm capable of producing rough clusters automatically without requiring the user to give as input the number of clusters to be produced. The efficiency of the algorithm in detecting the number of clusters present in the data set has been studied with the help of some real life data sets. Further, a nonparametric statistical analysis on the results of the experimental study has been carried out in order to analyze the efficiency of the proposed algorithm in automatic detection of the number of clusters in the data set with the help of rough version of Davies-Bouldin index.

scholarly journals Method for Measurement Uncertainty Evaluation of Cylindricity Error Based on Good Point Set

Procedia CIRP ◽  
2018 ◽  
Vol 75 ◽  
pp. 373-378
Author(s):  
Ren Lifei ◽  
Liu Ting ◽  
Zhao Qijian ◽  
Yang Jiangxin ◽  
Cao Yanlong
Keyword(s):  
Measurement Uncertainty ◽  
Uncertainty Evaluation ◽  
Good Point ◽  
Point Set ◽  
Cylindricity Error ◽  
Measurement Uncertainty Evaluation ◽  
Good Point Set
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