Comparison of Histogram Feature Based Thresholding with 3S Multi-Thresholding and Fuzzy C-Means

Introduction: Thresholding is one of the most important parts of segmentation whenever we want to detect a specific part of image. There are several thresholding methods that previous researchers used them frequently as bi-level techniques such as DBT or multilevel such as 3S. New histogram feature thresholding method is implemented to detect lesion area in digital mammograms and compared with 3S (Shrinking-Search-Space) multithresholding and FCM method in terms of segmentation quality and segmentation time as a benchmark in thresholding.Materials and Methods: These algorithms have been tested on 188 digital mammograms. Digital mammogram image used after preprocessing which was including crop the unnecessary area, resize the image into 1024 by 1024 pixel and then normalize pixel values by using simple contrast stretching method.Results: The results show that suggested method results are not similar with 3S and FCM methods, and it is faster than other methods. This is another superiority of suggested method with respect to others. Results of previous studies showed that FCM is not a reliable clustering algorithm and it needs several run to give us a reliable result (1). Results of this study also showed that this approach is correct.Conclusions: The suggested method may used as a reliable thresholding method in order to detection of lesion area.

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S-Canopy:A feature-based clustering algorithm for supplier categorization

2009 4th IEEE Conference on Industrial Electronics and Applications ◽

10.1109/iciea.2009.5138291 ◽

2009 ◽

Author(s):

Danish Irfan ◽

Xiaofei Xu ◽

Zengyou He ◽

Shengchun Deng ◽

Yunming Ye

Keyword(s):

Clustering Algorithm ◽

Feature Based

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Feature-Based Machining Precedence Reasoning and Sequence Planning

Volume 6: 18th Computers in Engineering Conference ◽

10.1115/detc98/cie-5707 ◽

1998 ◽

Author(s):

Yong Se Kim ◽

Eric Wang ◽

Choong Soo Lee ◽

Hyung Min Rho

Keyword(s):

Feature Recognition ◽

Search Space ◽

Machining Process ◽

Planning System ◽

Process Information ◽

Computer Aided Process Planning ◽

Sequence Planning ◽

Precedence Relations ◽

Feature Based ◽

Form Features

Abstract This paper presents a feature-based method to support machining sequence planning. Precedence relations among machining operations are systematically generated based on geometric information, tolerance specifications, and machining expertise. The feature recognition method using Alternating Sum of Volumes With Partitioning (ASVP) Decomposition is applied to obtain a Form Feature Decomposition (FFD) of a part model. Form features are classified into a taxonomy of atomic machining features, to which machining process information has been associated. Geometry-based precedence relations between features are systematically generated using the face dependency information obtained by ASVP Decomposition and the features’ associated machining process information. Multiple sets of precedence relations are generated as alternative precedence trees, based on the feature types and machining process considerations. These precedence trees are further enhanced with precedence relations from tolerance specifications and machining expertise. Machining sequence planning is performed for each of these precedence trees, applying a matrix-based method to reduce the search space while minimizing the number of tool changes. The precedence trees may then be evaluated based on machining cost and other criteria. The precedence reasoning module and operation sequence planning module are currently being implemented within a comprehensive Computer-Aided Process Planning system.

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Image Retrieval Using Data Mining Technique

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.8.26 ◽

2020 ◽

pp. 2115-2125

Author(s):

Sarmad T. Abdul-Samad ◽

Sawsan Kamal

Keyword(s):

Image Retrieval ◽

User Satisfaction ◽

Clustering Algorithm ◽

Retrieval System ◽

Spatial Information ◽

Search Space ◽

Extraction Process ◽

Retrieval Process ◽

Data Mining Technique ◽

Image Retrieval System

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.

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Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i06.6545 ◽

2020 ◽

Vol 34 (06) ◽

pp. 9908-9915

Author(s):

Sarah Keren ◽

Haifeng Xu ◽

Kofi Kwapong ◽

David Parkes ◽

Barbara Grosz

Keyword(s):

Incomplete Information ◽

Recognition Task ◽

Search Space ◽

Perfect Information ◽

Suggested Method ◽

Information Revelation ◽

Goal Recognition ◽

Effectiveness And Efficiency

We extend goal recognition design to account for partially informed agents. In particular, we consider a two-agent setting in which one agent, the actor, seeks to achieve a goal but has only incomplete information about the environment. The second agent, the recognizer, has perfect information and aims to recognize the actor's goal from its behavior as quickly as possible. As a one-time offline intervention and with the objective of facilitating the recognition task, the recognizer can selectively reveal information to the actor. The problem of selecting which information to reveal, which we call information shaping, is challenging not only because the space of information shaping options may be large, but also because more information revelation need not make it easier to recognize an agent's goal. We formally define this problem, and suggest a pruning approach for efficiently searching the search space. We demonstrate the effectiveness and efficiency of the suggested method on standard benchmarks.

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Clustering Algorithm for Vehicle’s Driving Data Feature based on Integrated Navigation

International Journal of Vehicle Structures and Systems ◽

10.4273/ijvss.13.4.14 ◽

2021 ◽

Vol 13 (4) ◽

Author(s):

Na Guo ◽

Yiyi Zhu

Keyword(s):

Clustering Algorithm ◽

Principal Component ◽

Kernel Principal Component Analysis ◽

Integrated Navigation ◽

Clustering Problem ◽

Incremental Method ◽

Feature Parameters ◽

Vehicle Acceleration ◽

Feature Based ◽

Clustering Problems

The clustering result of K-means clustering algorithm is affected by the initial clustering center and the clustering result is not always global optimal. Therefore, the clustering analysis of vehicle’s driving data feature based on integrated navigation is carried out based on global K-means clustering algorithm. The vehicle mathematical model based on GPS/DR integrated navigation is constructed and the vehicle’s driving data based on GPS/DR integrated navigation, such as vehicle acceleration, are collected. After extracting the vehicle’s driving data features, the feature parameters of vehicle’s driving data are dimensionally reduced based on kernel principal component analysis to reduce the redundancy of feature parameters. The global K-means clustering algorithm converts clustering problem into a series of sub-cluster clustering problems. At the end of each iteration, an incremental method is used to select the next cluster of optimal initial centers. After determining the optimal clustering number, the feature clustering of vehicle’s driving data is completed. The experimental results show that the global K-means clustering algorithm has a clustering error of only 1.37% for vehicle’s driving data features and achieves high precision clustering for vehicle’s driving data features.

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An Ontology Based Model for Document Clustering

International Journal of Intelligent Information Technologies ◽

10.4018/jiit.2011070105 ◽

2011 ◽

Vol 7 (3) ◽

pp. 54-69 ◽

Cited By ~ 13

Author(s):

U. K. Sridevi ◽

N. Nagaveni

Keyword(s):

Vector Space ◽

Domain Knowledge ◽

Clustering Algorithm ◽

Document Clustering ◽

Vector Space Model ◽

Search Space ◽

Space Model ◽

Clustering Model ◽

Document Collection ◽

Improved Performance

Clustering is an important topic to find relevant content from a document collection and it also reduces the search space. The current clustering research emphasizes the development of a more efficient clustering method without considering the domain knowledge and user’s need. In recent years the semantics of documents have been utilized in document clustering. The discussed work focuses on the clustering model where ontology approach is applied. The major challenge is to use the background knowledge in the similarity measure. This paper presents an ontology based annotation of documents and clustering system. The semi-automatic document annotation and concept weighting scheme is used to create an ontology based knowledge base. The Particle Swarm Optimization (PSO) clustering algorithm can be applied to obtain the clustering solution. The accuracy of clustering has been computed before and after combining ontology with Vector Space Model (VSM). The proposed ontology based framework gives improved performance and better clustering compared to the traditional vector space model. The result using ontology was significant and promising.

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DDoS Attack Security Situation Assessment Model Using Fusion Feature Based on Fuzzy C-Means Clustering Algorithm

Cloud Computing and Security - Lecture Notes in Computer Science ◽

10.1007/978-3-030-00009-7_59 ◽

2018 ◽

pp. 654-669

Author(s):

Ruizhi Zhang ◽

Jieren Cheng ◽

Xiangyan Tang ◽

Qiang Liu ◽

Xiangfeng He

Keyword(s):

Clustering Algorithm ◽

Assessment Model ◽

Situation Assessment ◽

Fuzzy C Means ◽

Ddos Attack ◽

Fuzzy C Means Clustering ◽

Feature Based ◽

Fusion Feature

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Analyzing cloud based reviews for product ranking using feature based clustering algorithm

Cluster Computing ◽

10.1007/s10586-018-1996-3 ◽

2018 ◽

Vol 22 (S3) ◽

pp. 6977-6984 ◽

Cited By ~ 2

Author(s):

N. Gobi ◽

A. Rathinavelu

Keyword(s):

Clustering Algorithm ◽

Feature Based ◽

Product Ranking

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Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers

Bioinformatics ◽

10.1093/bioinformatics/btaa613 ◽

2020 ◽

Vol 36 (20) ◽

pp. 5027-5036 ◽

Cited By ~ 3

Author(s):

Mingzhou Song ◽

Hua Zhong

Keyword(s):

Clustering Algorithm ◽

Human Cancer ◽

Clustering Algorithms ◽

Search Space ◽

R Package ◽

Supplementary Information ◽

Diagnostic Biomarkers ◽

Cancer Types ◽

Molecular Patterns ◽

Pan Cancer

Abstract Motivation Chromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality and reproducibility. Results We present the chromosome clustering method, establish its optimality and runtime and evaluate its performance. It uses dynamic programming enhanced with an algorithm to reduce search-space in-place to decrease runtime overhead. Using the method, we delineated outstanding genomic zones in 17 human cancer types. We identified strong continuity in dysregulation polarity—dominance by either up- or downregulated genes in a zone—along chromosomes in all cancer types. Significantly polarized dysregulation zones specific to cancer types are found, offering potential diagnostic biomarkers. Unreported previously, a total of 109 loci with conserved dysregulation polarity across cancer types give insights into pan-cancer mechanisms. Efficient chromosomal clustering opens a window to characterize molecular patterns in cancer genome and beyond. Availability and implementation Weighted univariate clustering algorithms are implemented within the R package ‘Ckmeans.1d.dp’ (4.0.0 or above), freely available at https://cran.r-project.org/package=Ckmeans.1d.dp. Supplementary information Supplementary data are available at Bioinformatics online.

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