Determination of Optimal Clusters Using a Genetic Algorithm

2008 ◽  
pp. 98-117 ◽  
Author(s):  
Tushar ◽  
Tushar ◽  
Shibendu Shekhar Roy ◽  
Dilip Kumar Pratihar
Keyword(s):  
Genetic Algorithm ◽  
Threshold Value ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Fcm Algorithm ◽  
Data Points ◽  
The Relationship

Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters based on the similarity among themselves. Clusters may be either crisp or fuzzy in nature. The present chapter deals with clustering of some data sets using Fuzzy C-Means (FCM) algorithm and Entropy-based Fuzzy Clustering (EFC) algorithm. In FCM algorithm, the nature and quality of clusters depend on the pre-defined number of clusters, level of cluster fuzziness and a threshold value utilized for obtaining the number of outliers (if any). On the other hand, the quality of clusters obtained by the EFC algorithm is dependent on a constant used to establish the relationship between the distance and similarity of two data points, a threshold value of similarity and another threshold value used for determining the number of outliers. The clusters should ideally be distinct and at the same time compact in nature. Moreover, the number of outliers should be as minimum as possible. Thus, the above problem may be posed as an optimization problem, which will be solved using a Genetic Algorithm (GA). The best set of multi-dimensional clusters will be mapped into 2-D for visualization using a Self-Organizing Map (SOM).

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

2008 ◽  
Vol 07 (04) ◽  
pp. 363-383 ◽  
Author(s):  
MUSTAPHA LEBBAH ◽  
YOUNÈS BENNANI ◽  
NICOLETA ROGOVSCHI
Keyword(s):  
Binary Data ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Binary Coding ◽  
Public Data ◽  
Multivariate Binary Data ◽  
Self Organizing

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

scholarly journals A dynamic K-means clustering for data mining

2019 ◽  
Vol 13 (2) ◽  
pp. 521
Author(s):  
Md. Zakir Hossain ◽  
Md.Nasim Akhtar ◽  
R.B. Ahmad ◽  
Mostafijur Rahman
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Large Data ◽  
Threshold Value ◽  
Specific Pattern ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Number Of Clusters ◽  
Data Points

<span>Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets.  The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed.The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.</span>

The use of CCD area detectors in charge-density research. Application to a mineral compound: the α-spodumene LiAl(SiO3)2

1999 ◽  
Vol 55 (6) ◽  
pp. 867-881 ◽  
Author(s):  
Sandrine Kuntzinger ◽  
Slimane Dahaoui ◽  
Nour Eddine Ghermani ◽  
Claude Lecomte ◽  
Judith A. K. Howard
Keyword(s):  
Electron Density ◽  
Silicon Oxide ◽  
Data Sets ◽  
X Ray Diffraction ◽  
Data Set ◽  
Charge Coupled Device ◽  
High Resolution Data ◽  
Aluminosilicate Mineral

X-ray diffraction data sets collected on both Nonius and Siemens (Bruker) goniometers equipped with charge-coupled device (CCD) area detectors have been tested for the electron-density determination of the aluminosilicate mineral compound α-spodumene LiAl(SiO3)2, aluminium lithium silicon oxide. Data collection strategies, reflection intensity peak integration methods and experimental error estimates are different for the two instruments. Therefore, the consistency and quality of the two types of CCD measurements have been carefully compared to each other and to high-resolution data collected on a conventional CAD-4 point-detector diffractometer. Multipole density model refinements were carried out against the CCD data and the statistical factors analysed in terms of experimental weighting schemes based on the standard uncertainties of the diffraction intensities derived by the Nonius and Siemens software programs. Consistent experimental electron-density features in the Si–O–Si and Si–O–Al bridges were found from both CCD data sets. The net atomic charges obtained from the kappa refinements against each CCD data set are also in good agreement and quite comparable with the results of the conventional CAD-4 experiment.

Mandatory and Default Rules in Fiduciary Law

2019 ◽  
pp. 434-448
Author(s):  
Daniel Clarry
Keyword(s):  
Modern Trend ◽  
Legal Institutions ◽  
Core Element ◽  
Constituent Element ◽  
Mandatory Rules ◽  
Default Rules ◽  
Other Regarding ◽  
The Relationship

This chapter discusses the nature and operation of mandatory and default rules in fiduciary law, arguing that loyalty is a core element of every fiduciary legal institution. Loyalty is the hallmark of fiduciary law, as it requires persons in other-regarding positions of power to perform functions selflessly, rather than selfishly. However, there are many circumstances in which a person undertakes and exercises other-regarding powers, underscoring the fact that a broad range of persons may be the subject of fiduciary law. This chapter first provides an overview of key concepts and context, focusing on the distinction between mandatory rules and default rules as well as sources of such rules in fiduciary law. It then considers fiduciary loyalty, citing examples that illustrate how a baseline of fiduciary accountability is implied by the essential nature of fiduciary legal institutions, along with the mandatory or default quality of the duties of care and good faith. The main thesis of this chapter is that loyalty is a basic constituent element of all fiduciary legal institutions. Whether fiduciary principles are mandatory involves a consideration and determination of whether the relationship or institution is inherently fiduciary as matter of law and legal classification. It also highlights the modern trend toward codification and clear legislative demarcation of mandatory and default rules in fiduciary law.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

Iranian Coaches Forgiveness and Relationship Quality to Their Athletes

2021 ◽  
pp. 1-8
Author(s):  
Hassan Gharayagh Zandi ◽  
Sahar Zarei ◽  
Mohammad Ali Besharat ◽  
Davoud Houminiyan sharif abadi ◽  
Ahmad Bagher Zadeh
Keyword(s):  
Relationship Quality ◽  
Structural Equation ◽  
Equation Modeling ◽  
Data Sets ◽  
Mediating Role ◽  
Interpersonal Behaviors ◽  
Key Factor ◽  
The Relationship

Coaching has often been viewed as a context within which coaches operate to largely bring about changes in athlete’s performance and flourishing. One key factor to successful outcomes in coaching is the quality of the relationship between coaches and athletes. The coach–athlete relationship is at the heart of coaching; however, limited studies have been conducted on its antecedents. The aim of this study was to investigate the relationship between coaches’ forgiveness and perceived relationship quality toward their athletes through verifying the mediating role of interpersonal behaviors of coaches. A total of 270 Iranian coaches participated in the survey, and the data sets were analyzed using structural equation modeling. Results revealed that forgiveness positively predicted the coaches’ perceived relationship quality with their athletes, and this pathway was mediated by the coaches’ interpersonal behaviors.

A Support Based Initialization Algorithm for Categorical Data Clustering

2018 ◽  
Vol 11 (2) ◽  
pp. 53-67
Author(s):  
Ajay Kumar ◽  
Shishir Kumar
Keyword(s):  
Categorical Data ◽  
Selection Process ◽  
Numerical Data ◽  
Real Data ◽  
Data Sets ◽  
Data Set ◽  
Data Object ◽  
Data Points ◽  
Wu Method ◽  
Selection Algorithms

Several initial center selection algorithms are proposed in the literature for numerical data, but the values of the categorical data are unordered so, these methods are not applicable to a categorical data set. This article investigates the initial center selection process for the categorical data and after that present a new support based initial center selection algorithm. The proposed algorithm measures the weight of unique data points of an attribute with the help of support and then integrates these weights along the rows, to get the support of every row. Further, a data object having the largest support is chosen as an initial center followed by finding other centers that are at the greatest distance from the initially selected center. The quality of the proposed algorithm is compared with the random initial center selection method, Cao's method, Wu method and the method introduced by Khan and Ahmad. Experimental analysis on real data sets shows the effectiveness of the proposed algorithm.

scholarly journals Children with 5′-end NF1 gene mutations are more likely to have glioma

2017 ◽  
Vol 3 (5) ◽  
pp. e192 ◽  
Author(s):  
Corina Anastasaki ◽  
Stephanie M. Morris ◽  
Feng Gao ◽  
David H. Gutmann
Keyword(s):  
Gene Mutation ◽  
Statistical Significance ◽  
Gene Mutations ◽  
Neurofibromatosis Type ◽  
Published Data ◽  
Data Sets ◽  
Nonsense Mutations ◽  
Data Set ◽  
Nf1 Gene ◽  
The Relationship

Objective:To ascertain the relationship between the germline NF1 gene mutation and glioma development in patients with neurofibromatosis type 1 (NF1).Methods:The relationship between the type and location of the germline NF1 mutation and the presence of a glioma was analyzed in 37 participants with NF1 from one institution (Washington University School of Medicine [WUSM]) with a clinical diagnosis of NF1. Odds ratios (ORs) were calculated using both unadjusted and weighted analyses of this data set in combination with 4 previously published data sets.Results:While no statistical significance was observed between the location and type of the NF1 mutation and glioma in the WUSM cohort, power calculations revealed that a sample size of 307 participants would be required to determine the predictive value of the position or type of the NF1 gene mutation. Combining our data set with 4 previously published data sets (n = 310), children with glioma were found to be more likely to harbor 5′-end gene mutations (OR = 2; p = 0.006). Moreover, while not clinically predictive due to insufficient sensitivity and specificity, this association with glioma was stronger for participants with 5′-end truncating (OR = 2.32; p = 0.005) or 5′-end nonsense (OR = 3.93; p = 0.005) mutations relative to those without glioma.Conclusions:Individuals with NF1 and glioma are more likely to harbor nonsense mutations in the 5′ end of the NF1 gene, suggesting that the NF1 mutation may be one predictive factor for glioma in this at-risk population.

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