Fuzzy C-Means Classifier for Soil Data
09 Oct 2010-International Journal of Computer Applications (Foundation of Computer Science FCS)-Vol. 6, Iss: 4, pp 1-5
TL;DR: An index of fuzzy soil classification generated by Fuzzy C-means classification is presented, capable of handling the uncertainty existing in soil parameters and can be successfully applied to classify soils.
Abstract: The distribution of soil classes is an important factor in agricultural soils. In order to generate the soil classification, fuzzy soil classifications were developed to provide the means to characterize and quantify the soil classes. This paper presents an index of fuzzy soil classification generated by Fuzzy C-means classification. The ability of classification of the soils is tested with a Soil database. Fuzzy c-means approach is also capable of handling the uncertainty existing in soil parameters. As a result, fuzzy c-means clustering can be successfully applied to classify soils.
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TL;DR: The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researched.
Abstract: It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in geotechnical engineering, during those years many (AI) techniques were developed based in mathematical, statistical and logical concepts, but the breakthrough occurs by mimicking the natural searching and optimization algorithms. This huge development in (AI) techniques reflected on the geotechnical engineering problems. In this research, 626 paper and thesis published in the period from 1984 to 2019 concerned in applying (AI) techniques in geotechnical engineering were collected, filtered, arranged and classified with respect to subject, (AI) technique, publisher and publishing date and stored in a database. The extracted information from the database were tabulated, presented graphically and commented. The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researches.
56 citations
Additional excerpts
...…et al. (2019a) 430 Samui and Sitharam (2011b) 470 Viswanathan and Pijush (2016) 351 Martins and Randa (2013) 391 Koopialipoor et al. (2019b) 431 Bhargavi and Jyothi (2010) 471 Ranasinghe et al. (2018) 352 Mokhtar and Mahmoud (2018) 392 Al-Neami and Ami (2015) 432 Camarinha et al. (2011)…...
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01 Jan 2011
TL;DR: A comparative study of developed algorithms for classifying soil texture in agriculture soil data is given.
Abstract: Soil Classification deals with the systematic categorization of soils based on distinguished characteristics as well as criteria. We developed Data Mining techniques like: GATree, Fuzzy Classification rules and Fuzzy C - Means algorithm for classifying soil texture in agriculture soil data. In this paper, we give a comparative study of developed algorithms. The study is used to compare and analyze the soil data.
31 citations
TL;DR: This article directs on crop yield forecast in Trichy district by adopting data mining techniques for rule formation on classifying the training data and implementing prediction for test data, which employs fuzzy C means algorithm for clustering and multilayer perceptron design for prediction.
Abstract: The agricultural stock depends upon several factors like biological, seasonal, and economic determinants. The growers sustain a vital loss if they are not capable of predicting the variations in these circumstances. The uncertainty on crop yield can be predicted in a logical and mathematical way. The forecast is made based on the previous archives of yield data secured from that area. Data mining is one such procedure practised to predict the crop yield. The systems examine the data, and on mining, several patterns based on numerous parameters predict the return. This article directs on crop yield forecast in Trichy district by adopting data mining techniques for rule formation on classifying the training data and implementing prediction for test data. The suggested method employs fuzzy C means algorithm for clustering and multilayer perceptron design for prediction. The results of accuracy and execution time of the proposed system correlated with the regression algorithm of prediction.
11 citations
TL;DR: Experimental results proved that applying the fuzzy value of memberships to Euclidian calculations in the FCM and SVM techniques has better accuracy than the ordinary calculating method and just ignoring the unselected features.
Abstract: Euclidian calculations represent a cornerstone in many machine learning techniques such as the Fuzzy C-Means (FCM) and Support Vector Machine (SVM) techniques The FCM technique calculates the Euclidian distance between different data points, and the SVM technique calculates the dot product of two points in the Euclidian space These calculations do not consider the degree of relevance of the selected features to the target class labels This paper proposed a modification in the Euclidian space calculation for the FCM and SVM techniques based on the ranking of features extracted from evaluating the features The authors consider the ranking as a membership value of this feature in Fuzzification of Euclidian calculations rather than using the crisp concept of feature selection, which selects some features and ignores others Experimental results proved that applying the fuzzy value of memberships to Euclidian calculations in the FCM and SVM techniques has better accuracy than the ordinary calculating method and just ignoring the unselected features
8 citations
Dissertation•
01 Jan 2012
TL;DR: In this study, an attempt was made to propose and evaluate the principal component based fuzzy c-means algorithm in classifying 518 lentil genotypes based on their numeric agronomic and morphological traits.
Abstract: Cluster analysis is one of the unsupervised pattern recognition techniques that can be used to organize data into groups based on similarities among the individual data items. In this study, an attempt was made to propose and evaluate the principal component based fuzzy c-means algorithm in classifying 518 lentil genotypes based on their numeric agronomic and morphological traits. The optimum number of clusters was obtained using validity measures.
2 citations
Additional excerpts
...Bhargavi and Jyothi (2010) have successfully applied fuzzy c-means to provide the means to characterize and quantify the soil classes....
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References
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TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.
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"Fuzzy C-Means Classifier for Soil D..." refers background in this paper
...Soil classification is a dynamic subject, from the structure of the system itself, to the definitions of classes, and finally in the application in the field....
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...Soil characteristics are single parameters which are observable or measurable in the field or laboratory, or can be analyzed using microscope techniques....
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TL;DR: ISODATA, a novel method of data analysis and pattern classification, is described in verbal and pictorial terms, in terms of a two-dimensional example, and by giving the mathematical calculations that the method uses.
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1,080 citations
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TL;DR: Lotfi Zadeh (1965) introduced fuzzy set theory and fuzzy logic, and promoted these as a way of reasoning about uncertainty in computer systems.
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901 citations
Book•
01 Jan 1994
TL;DR: A rigorous study of the principles of fuzzy set theory supports the book's fundamental aim, which is to promote the development of fuzzy systems for successful real-world applications.
Abstract: From the Publisher:
The strength of this book lies in its clear and precise examination of the theory of fuzzy systems. A rigorous study of the principles of fuzzy set theory supports the book's fundamental aim, which is to promote the development of fuzzy systems for successful real-world applications. The authors highlight two important application areas: approximate reasoning in knowledge-based systems, and fuzzy control. Reflecting the state of the art in fuzzy systems research, the book is both comprehensive and practical in its approach. Its illustration of key concepts is based on a detailed analysis of the underlying semantics. Each chapter is enhanced by useful historical notes and extensive references. The book presents several industrial case studies and exercises designed to increase its appeal to advanced students and researchers in computer science, applied mathematics and engineering.
587 citations