Relevant bibliographies by topics / Fuzzy partition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Fuzzy partition'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Fuzzy partition"

1

Mu, Yashuang, Lidong Wang, and Xiaodong Liu. "Dynamic programming based fuzzy partition in fuzzy decision tree induction." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6757–72. http://dx.doi.org/10.3233/jifs-191497.

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Fuzzy decision trees are one of the most popular extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Among the majority of fuzzy decision trees learning methods, the number of fuzzy partitions is given in advance, that is, there are the same amount of fuzzy items utilized in each condition attribute. In this study, a dynamic programming-based partition criterion for fuzzy items is designed in the framework of fuzzy decision tree induction. The proposed criterion applies an improved dynamic programming algorithm used in scheduling problems to establish an optimal number of fuzzy items for each condition attribute. Then, based on these fuzzy partitions, a fuzzy decision tree is constructed in a top-down recursive way. A comparative analysis using several traditional decision trees verify the feasibility of the proposed dynamic programming based fuzzy partition criterion. Furthermore, under the same framework of fuzzy decision trees, the proposed fuzzy partition solution can obtain a higher classification accuracy than some cases with the same amount of fuzzy items.
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Li, Chun Sheng, and Hong Liang Dai. "On the Measure of Compactness of Fuzzy Clustering." Advanced Materials Research 204-210 (February 2011): 1403–6. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1403.

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This paper tested the measures of compactness of fuzzy partitions. Over the same labeled data, Fuzzy k-Means clustering algorithm generates the first partition, then the proposed revision function in (7) revises it several times to generate various fuzzy partitions with different pattern recognition rates computed by (6), finally the measures of compactness measure the compactness of each fuzzy partition. Experimental results on real data show that the measures of compactness in literatures fail to measure the compactness of a fuzzy clustering in some cases, for they argue that the fuzzy clustering with higher pattern recognition rate is less compact and worse than that with lower pattern recognition rate.
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Hyung Lee-Kwang and Keon-Myung Lee. "Fuzzy hypergraph and fuzzy partition." IEEE Transactions on Systems, Man, and Cybernetics 25, no. 1 (1995): 196–201. http://dx.doi.org/10.1109/21.362951.

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Honda, Katsuhiro, Shunnya Oshio, and Akira Notsu. "Fuzzy Co-Clustering Induced by Multinomial Mixture Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 717–26. http://dx.doi.org/10.20965/jaciii.2015.p0717.

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A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be properly tuned. The advantages of the dual fuzzy partition are demonstrated through several experimental results including document clustering applications.
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Malik, D. S., John N. Mordeson, and M. K. Sen. "Admissible Partitions of Fuzzy Finite State Machines." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 06 (December 1997): 723–32. http://dx.doi.org/10.1142/s021848859700052x.

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In this paper we introduce the concept of a covering of a ffsm by another, admissible partitions and relations of a ffsm, μ-orthogonality of admissible partitions, irreducibile ffsm, and the quotient of a ffsm induced by an admissible partition of the state set.
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Zuo, Yong Xia, Guo Qiang Wang, and Chun Cheng Zuo. "The Segmentation Algorithm for Pavement Cracking Images Based on the Improved Fuzzy Clustering." Applied Mechanics and Materials 319 (May 2013): 362–66. http://dx.doi.org/10.4028/www.scientific.net/amm.319.362.

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The segmentation technology of pavement cracking image is critical for identifying, quantifying and classifying pavement cracks. An improved fuzzy clustering algorithm is introduced to segment pavement cracking images. The algorithm makes no assumptions the initial position of clusters. For each value of the multiscale parameter, it obtains a corresponding hard partition. The different partitions values of the multiscale parameter indicate the structure of the image in different partitional scales. The algorithm was tested on actual pavement cracking images. We compared the results with FCM and OTSU to show that the improved fuzzy clustering algorithm can provide better crack edges.
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Jung, Hye-Young, Woo-Joo Lee, and Seung Hoe Choi. "Fuzzy regression model using fuzzy partition." Journal of Physics: Conference Series 1334 (October 2019): 012019. http://dx.doi.org/10.1088/1742-6596/1334/1/012019.

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Ma, Ming, I. B. Turksen, and Abraham Kandel. "Fuzzy partition and fuzzy rule base." Information Sciences 108, no. 1-4 (July 1998): 109–21. http://dx.doi.org/10.1016/s0020-0255(97)10061-5.

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Pop, Horia F., Tudor L. Pop, and Costel Sarbu. "Assessment of Heart Disease using Fuzzy Classification Techniques." Scientific World JOURNAL 1 (2001): 369–90. http://dx.doi.org/10.1100/tsw.2001.64.

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In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients.
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Gordon, A. D., and M. Vichi. "Fuzzy partition models for fitting a set of partitions." Psychometrika 66, no. 2 (June 2001): 229–47. http://dx.doi.org/10.1007/bf02294837.

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Dissertations / Theses on the topic "Fuzzy partition"

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Dimitriadou, Evgenia, Andreas Weingessel, and Kurt Hornik. "Fuzzy voting in clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/742/1/document.pdf.

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In this paper we present a fuzzy voting scheme for cluster algorithms. This fuzzy voting method allows us to combine several runs of cluster algorithms resulting in a common fuzzy partition. This helps us to overcome instabilities of the cluster algorithms and results in a better clustering.
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." University of Sydney. School of Electrical and Information Engineering, 2005. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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Quéré, Romain. "Quelques propositions pour la comparaison de partitions non strictes." Phd thesis, Université de La Rochelle, 2012. http://tel.archives-ouvertes.fr/tel-00950514.

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Cette thèse est consacrée au problème de la comparaison de deux partitions non strictes (floues/probabilistes, possibilistes) d'un même ensemble d'individus en plusieurs clusters. Sa résolution repose sur la définition formelle de mesures de concordance reprenant les principes des mesures historiques développées pour la comparaison de partitions strictes et trouve son application dans des domaines variés tels que la biologie, le traitement d'images, la classification automatique. Selon qu'elles s'attachent à observer les relations entre les individus décrites par chacune des partitions ou à quantifier les similitudes entre les clusters qui composent ces partitions, nous distinguons deux grandes familles de mesures pour lesquelles la notion même d'accord entre partitions diffère, et proposons d'en caractériser les représentants selon un même ensemble de propriétés formelles et informelles. De ce point de vue, les mesures sont aussi qualifiées selon la nature des partitions comparées. Une étude des multiples constructions sur lesquelles reposent les mesures de la littérature vient compléter notre taxonomie. Nous proposons trois nouvelles mesures de comparaison non strictes tirant profit de l'état de l'art. La première est une extension d'une approche stricte tandis que les deux autres reposent sur des approches dite natives, l'une orientée individus, l'autre orientée clusters, spécifiquement conçues pour la comparaison de partitions non strictes. Nos propositions sont comparées à celles de la littérature selon un plan d'expérience choisi pour couvrir les divers aspects de la problématique. Les résultats présentés montrent l'intérêt des propositions pour le thème de recherche qu'est la comparaison de partitions. Enfin, nous ouvrons de nouvelles perspectives en proposant les prémisses d'un cadre qui unifie les principales mesures non strictes orientées individus.
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Talwanga, Matiki. "Counting of finite fuzzy subsets with applications to fuzzy recognition and selection strategies." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018186.

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The counting of fuzzy subsets of a finite set is of great interest in both practical and theoretical contexts in Mathematics. We have used some counting techniques such as the principle of Inclusion-Exclusion and the Mõbius Inversion to enumerate the fuzzy subsets of a finite set satisfying different conditions. These two techniques are interdependent with the M¨obius inversion generalizing the principle of Inclusion-Exclusion. The enumeration is carried out each time we redefine new conditions on the set. In this study one of our aims is the recognition and identification of fuzzy subsets with same features, characteristics or conditions. To facilitate such a study, we use some ideas such as the Hamming distance, mid-point between two fuzzy subsets and cardinality of fuzzy subsets. Finally we introduce the fuzzy scanner of elements of a finite set. This is used to identify elements and fuzzy subsets of a set. The scanning process of identification and recognition facilitates the choice of entities with specified properties. We develop a procedure of selection under the fuzzy environment. This allows us a framework to resolve conflicting issues in the market place.
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Li, Hung Chun, and 李泓鈞. "A Fuzzy Space Propagating Auto-Partition Mechanism for the Fuzzy Causal Network." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/72065359514831741144.

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SHI, CHUN-YAN, and 施淳諺. "Fuzzy partition and allocation for multi chip module." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/02843266166060500565.

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Chang, Yung-Chuan, and 張永泉. "Apply Auto-Partition to Build a Learnmental Model upon Fuzzy Causal Network." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/71686587461723993546.

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碩士
清雲科技大學
電子工程研究所
95
In order to fit in humanity, the non-quantification and fuzzified methods are used to evaluate the learning performance of learner in mostly intelligent digital tutorial systems. Therefore, the fuzzy causal network model of learner used in those systems, but the following problems are existed while using the fuzzy inference in a multi-layer causal network with partial feedback: (a) There are too many membership functions need to be assigned and adjusted; (b) Above the second hidden layer, there is not physical meaning to assign and adjust those fuzzy partitions with inference independently. Dearing with those problems, a fuzzy space partition propagation method is designed, and the associated inference method also used in a multi-layer fuzzy causal network. This method has the following advantages. (a) The system will be automatically to adjust the membership function. (b) The consequent of inference in previous layer just as the antecedent part of inference in the posterior layer. This method can reduce the difficulty of artificial partition, and make the digital tutorial systems more flexible and more intelligent.
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Kao, Chih-Jen, and 高志仁. "Design of Accurate and Compact Fuzzy Rule-Based Classifiers Using Evolutionary Grid Partition of Feature Space." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/bcy6gx.

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碩士
逢甲大學
資訊工程所
91
This thesis proposes an evolutionary approach to designing the accurate classifier with a compact fuzzy-rule base using an intelligent genetic algorithm IGA and a grid partition of feature space. To design an accurate fuzzy classification system, the flexibility of membership functions is increased by using a parameterized trapezoidal membership function. Since the number of possible fuzzy rules is exponentially increased with the numbers of input features, it is an intractable task to obtain compact classifiers for high-dimensional classification problems, especially when the number of parameters in a membership function is increased. The design of fuzzy classifiers is formulated as a large parameter optimization problem with three objectives: 1) high classification ability, 2) small number of fuzzy rules, and 3) small total number of antecedent conditions. IGA hybrids the advantages of conventional genetic algorithms and orthogonal experimental design and can efficiently solve large parameter optimization problems. IGA and an efficient chromosome encoding are used to effectively solve the investigated problem, in which the membership function and fuzzy rule are simultaneously determined. Extensive computer simulations demonstrate that the proposed method is capable of efficiently solving classification problems to generate accurate and compact fuzzy classifiers with fuzzy rules of high interpretability.
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Chen, Hua-Ching, and 陳華慶. "Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/91523362416957788045.

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碩士
國立金門技術學院
電資研究所
98
The self-generation fuzzy density partitions algorithm is developed in this thesis. A particle swarm optimization (PSO) algorithm with the improvement of the fuzzy density measure is applied to generate correct clustering results in identifying their clusters for different data sets. In this proposed learning method, the divided individual fuzzy partitions can represent the feature of the clustering data set. Five artificial data sets are considered as testing patterns to demonstrate the efficiency of the proposed method. Simulations compared with other traditional K-means and Fuzzy C-means clustering algorithms demonstrate the high performance of the proposed self-generation fuzzy density partitions algorithm.
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Book chapters on the topic "Fuzzy partition"

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Verde, Rosanna, and Domenica Matranga. "A Semi-Fuzzy Partition Algorithm." In COMPSTAT, 483–88. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_67.

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Zhang, Hengshan, Tianhua Chen, Zhongmin Wang, and Yanpin Chen. "Novel Aggregation Functions Based on Domain Partition with Concentrate Region of Data." In Fuzzy Logic, 107–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_8.

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Intan, Rolly, and Masao Mukaidono. "Degree of Similarity in Fuzzy Partition." In Advances in Soft Computing — AFSS 2002, 20–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45631-7_3.

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Perfilieva, Irina, Michal Holčapek, and Vladik Kreinovich. "Adjoint Fuzzy Partition and Generalized Sampling Theorem." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 459–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40581-0_37.

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Szilágyi, László. "Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition." In Fuzzy Sets, Rough Sets, Multisets and Clustering, 101–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47557-8_7.

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Smits, Grégory, Olivier Pivert, and Toan Ngoc Duong. "On Dissimilarity Measures at the Fuzzy Partition Level." In Communications in Computer and Information Science, 301–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91476-3_25.

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Simiński, Krzysztof. "Patchwork Neuro-fuzzy System with Hierarchical Domain Partition." In Advances in Intelligent and Soft Computing, 11–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93905-4_2.

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Lago-Fernández, Luis F., Manuel Sánchez-Montañés, and Fernando Corbacho. "Fuzzy Cluster Validation Using the Partition Negentropy Criterion." In Artificial Neural Networks – ICANN 2009, 235–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_24.

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Nguyen, Van Thien, Long Giang Nguyen, and Nhu Son Nguyen. "Fuzzy Partition Distance Based Attribute Reduction in Decision Tables." In Rough Sets, 614–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99368-3_48.

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Liu, Jian. "Fuzzy Algorithm Based on Diffusion Maps for Network Partition." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 163–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14932-0_21.

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Conference papers on the topic "Fuzzy partition"

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Shamsizadeh, Marzieh, and Mohammad Mehdi Zahedi. "Intuitionistic admissible partition (Intuitionistic admissible partition)." In 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE, 2017. http://dx.doi.org/10.1109/cfis.2017.8003675.

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Wu, Kuo-Lung. "An analysis of partition index maximization algorithm." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277353.

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Guillaume, Serge, and Brigitte Charnomordic. "Fuzzy partition-based distance practical use and implementation." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622372.

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Kuo-Lung Wu. "An analysis of robustness of partition coefficient index." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630393.

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Shinkai, Kimiaki, Hajime Yamashitar, and Shuya Kanagawa. "Decision Analysis of Fuzzy Partition Tree Applying Fuzzy Theory." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.236.

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Qing, Ming. "Fuzzy Entropy of Fuzzy Partition on a Finite Interval." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.120.

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Nguyen, Linh, Irina Perfilieva, and Michal Holcapek. "Generalized Fuzzy Partition in Galerkin Method for the Boundary Valued Problem." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858997.

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Sarkar, Kaushik, and Nikhil R. Pal. "Is it rational to partition a data set using kernel-clustering?" In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007685.

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Mahnhoon Lee. "Fuzzy cluster validity index based on object proximities defined over fuzzy partition matrices." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630387.

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Abu-Mahfouz, Issam, Amit Banerjee, and A. H. M. Esfakur Rahman. "Surface Roughness Identification in End Milling Using Vibration Signals and Fuzzy Clustering." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-68207.

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Abstract:
The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.
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