Analysis of Individual Faith through Holy Bible Using Fuzzy Clustering

Devadoss, A. Victor; Rajkumar, A.; Jayalatha, C.
August 2013
International Journal of Computer Theory & Engineering;Aug2013, Vol. 5 Issue 4, p731
Academic Journal
As the world scenario has been witnessing different types of natural catastrophes, loss of human life, destruction of sustenance, people have grown cold and have lost hope. In an attempt to revive this lost faith, this paper studies the survey done on Individual Faith as identified from various incidences in the Holy Bible. Fuzzy Clustering which enables classification of data elements into different clusters based on varying degrees of a selected character was the selected methodology. Applying this, the events or data elements were grouped into various clusters based on varying degrees of Faith, on a scale from one to ten, i.e., High, Moderate and Low cluster groups supported by expert opinion on each category. Since clustering algorithms are useful in situations where little prior knowledge exists, this concept was applied to make a quantitative comparison of various degrees of faith.


Related Articles

  • Proposed a new Fuzzy C-Means Algorithm based on Electrical Rules. Golabzaei, Amir; Yaghoubizadeh, Mohsen; Mirkhanzadeh, Behruz // International Proceedings of Computer Science & Information Tech;2012, Vol. 49, p32 

    Every day, people deal with different types of data and different types of measurements and observations. Data describe the characteristics of data and summarize the result of experiment. Clustering or classification of data is an important activity to partitioning the data point in to...

  • A FCM Algorithm Based on Weighted Intuitionistic Fuzzy Set. Chang Yan; Chen Ai-dong // International Journal of Digital Content Technology & its Applic;Jun2012, Vol. 6 Issue 11, p95 

    To make up the limitations of existing intuitionistic fuzzy sets clustering, a new fuzzy C-means clustering algorithm (WIFCM) based on weighted intuitionistic fuzzy set was proposed. The concepts of equivalent classification object and weighted intuitionistic fuzzy set were put forward first,...

  • Improvement on A fuzzy c-means algorithm based on genetic algorithm. GuoChenJiang; ZhijianSun // Applied Mechanics & Materials;2014, Issue 614, p385 

    Weighting exponent m is an important parameter in fuzzy c-means(FCM) algorithm. In this paper, an approach based on genetic algorithm is proposed to improve the FCM clustering algorithm through the optimal choice of the parameter m. Experimental results show that the better clustering results...

  • ROZMYTA KLASYFIKACJA SPEKTRALNA C-ÅšREDNICH DLA DANYCH SYMBOLICZNYCH INTERWAŁOWYCH. Pełka, Marcin // Research Papers of the Wroclaw University of Economics / Prace N;2013, Issue 278, p282 

    The main aim of the paper is to present a proposal of new fuzzy clustering method for symbolic interval-valued data. The paper presents basic terms of symbolic data, spectral clustering and fuzzy c-means clustering. In the empirical part results of simulation study with application of artificial...

  • Measuring the performance of FCM versus PSO for fuzzy clustering problems. Jafari, Hamid Reza; Soltani, Amir Reza; Soltani, Mohammad Reza // International Journal of Industrial Engineering Computations;Winter2013, Vol. 4 Issue 1, p387 

    Clustering cellular manufacturing plays an important role in many industrial engineering problems. This paper investigates the performance of two methods of heuristic and metaheuristics fuzzy clustering. The proposed method investigates heuristic well-known FCM and particle swarm optimization...

  • Research on Cluster Analysis of High Dimensional Space Based on Fuzzy Extension. Shan Donghong; Li Wei Yao // Journal of Digital Information Management;Oct2013, Vol. 11 Issue 5, p359 

    Traditional spatial data are generally high dimensional features, and in the clustering of high dimensional data can be directly applied to data processing because of Dimension effect and the data sparseness problem. For CLIQUE algorithm, which usually have the problem such as prone to non-axis...

  • An Efficient Algorithm for Segmentation Using Fuzzy Local Information C-Means Clustering. Mekapothula, Sandeep Kumar; Kumar, V. Jai // International Journal of Computer Science & Network Security;Oct2012, Vol. 12 Issue 10, p139 

    This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can...

  • Footprint generation using fuzzy-neighborhood clustering. Parker, Jonathon; Downs, Joni // GeoInformatica;Apr2013, Vol. 17 Issue 2, p285 

    Geometric footprints, which delineate the region occupied by a spatial point pattern, serve a variety of functions in GIScience. This research explores the use of two density-based clustering algorithms for footprint generation. First, the Density-Based Spatial Clustering with Noise (DBSCAN)...

  • Performance Evaluation of K-Means and Fuzzy C-Means Clustering Algorithms for Identification of Hematoma in Brain CT scan Images. Sharma, Bhavna; Venugopalan, K. // International Journal of Advanced Research in Computer Science;Mar/Apr2012, Vol. 3 Issue 2, p219 

    Clustering is the assignment of a set of observations into subsets, called clusters so that observations in the same cluster are similar in some sense. This research paper deals with two of the most delegated clustering algorithms namely centroid based K-Means and Fuzzy C-Means for...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics