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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Vempaty, Lakshmi Namratha Tatiya, Manjusha Shrivastava, Anurag Kulkarni, Gururaj L. Singh, Pankaj Saxena, Vijay New York University Avis Budget Group South BrunswickNJ United States Indira College of Engineering and Management Department of Computer Engineering Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Vardhaman College of Engineering Shamshabed Computer Science and Engineering Hyderabad India Lloyd Law College Greater Noida India Lloyd Institute of Management and Technology Greater Noida India

ISBN: (纸本)9798350304480

Medical imaging analysis relies heavily on the segmentation of brain tumours to help in treatment planning, diagnosis and patient monitoring. The recent breakthroughs in artificial intelligence and deep learning approaches have revealed promising outcomes in automating this crucial process. The BRATS ('Multimodal Brain Tumour Segmentation Challenge') dataset is used throughout this study to investigate the potential of the agglomerative method in tandem with the VGG16 'convolutional neural network'architecture for brain tumor segmentation. Ground-truth tumor segmentation labels are provided with multimodal MRI scans from the BRATS dataset. These modalities include T1c, T1, FLAIR and T2. This research intends to improve clinical decision-making and patient care by increasing the precision and speed of brain tumor segmentation by the combined use of the agglomerative algorithm and VGG16. © 2023 IEEE.

关键词： agglomerative algorithm Brain tumor segmentation BRATS dataset Cross-entropy loss Deep learning techniques Ground truth annotations MRI scans Normalization VGG16

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Model-based clustering of multiple networks with a hierarchical algorithm

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STATISTICS AND COMPUTING 2024年第1期34卷 32-32页

作者： Rebafka, Tabea Univ Paris Cite Sorbonne Univ CNRS Lab Probabil Stat & Modelisat LPSM Paris France INRAE MaIAGE Jouy En Josas France

The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the collection of networks. Using a Bayesian framework, model selection is performed in an automated way since the algorithm stops when the best number of clusters is attained. The algorithm is computationally efficient, when carefully implemented. The aggregation of clusters requires a means to overcome the label-switching problem of the stochastic block model and to match the block labels of the networks. To address this problem, a new tool is proposed based on a comparison of the graphons of the associated stochastic block models. The clustering approach is assessed on synthetic data. An application to a set of ecological networks illustrates the interpretability of the obtained results.

关键词： Graph clustering Multiple networks Stochastic block model agglomerative algorithm Graphon distance Integrated classification likelihood

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MPOC: an agglomerative algorithm for multicriteria partially ordered clustering

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2013年第3期11卷 253-273页

作者： Rocha, Clara Dias, Luis C. INESC Coimbra P-3000033 Coimbra Portugal Inst Politecn Coimbra Escola Super Tecnol Saude Coimbra P-3040162 Coimbra Portugal Univ Coimbra Fac Econ P-3004512 Coimbra Portugal

In the field of multicriteria decision aid, considerable attention has been paid to supervised classification problems where the purpose is to assign alternatives into predefined ordered classes. In these approaches, often referred to as sorting methods, it is usually assumed that classes are either known a priori or can be identified by the decision maker. On the other hand, when the objective is to identify groups (clusters) of alternatives sharing similar characteristics, the problem is known as a clustering problem, also called an unsupervised learning problem. This paper proposes an agglomerative clustering method based on a crisp outranking relation. The method regroups alternatives into partially ordered classes, based on a quality of partition measure which reflects the percentage of pairs of alternatives that are compatible with a decision-maker's multicriteria preference model.

关键词： Multi-criteria decision aiding (MCDA) Sorting problem Clustering Outranking relations agglomerative algorithm Partial ranking

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agglomerative oversegmentation using dual similarity and entropy rate

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PATTERN RECOGNITION 2019年 93卷 324-336页

作者： Ni, Huan Niu, Xiaonan Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Jiangsu Peoples R China China Geol Survey Nanjing Ctr Nanjing 210016 Jiangsu Peoples R China

Oversegmentation is a preprocessing step for many computer vision and remote sensing tasks, such as object recognition and image understanding. In this paper, a method called agglomerative oversegmentation (AOS) is proposed. AOS first designs a dual similarity of the connected pixels by considering both the pixel and region levels. The entropy rate of random walks is then employed on the image plane, and the gain of each pair of connected pixels is estimated. Finally, a fast agglomerative algorithm is developed to oversegment the image using an entropy rate gain function. Two challenging datasets are utilized in the experiments which show the promising performance of AOS. In contrast to the state-of-the-art algorithms, the oversegmentation of AOS is more consistent with the geometric structures of the target objects, especially for the objects with complex texture on remote sensing images. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Oversegmentation agglomerative algorithm Entropy rate Remote sensing

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Customer Segmentation using K-means Clustering 1

Customer Segmentation using K-means Clustering

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1st International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)

作者： Kansal, Tushar Bahuguna, Suraj Singh, Vishal Choudhury, Tanupriya UPES Dept Informat Sch Comp Sci Dehra Dun Uttar Pradesh India

ISBN: (纸本)9781538677094

The zeitgeist of modern era is innovation, where everyone is embroiled into competition to be better than others. Today's business run on the basis of such innovation having ability to enthral the customers with the products, but with such a large raft of products leave the customers confounded, what to buy and what to not and also the companies are nonplussed about what section of customers to target to sell their products. This is where machine learning comes into play, various algorithms are applied for unravelling the hidden patterns in the data for better decision making for the future. This elude concept of which segment to target is made unequivocal by applying segmentation. The process of segmenting the customers with similar behaviours into the same segment and with different patterns into different segments is called customer segmentation. In this paper, 3 different clustering algorithms (k-Means, agglomerative, and Meanshift) are been implemented to segment the customers and finally compare the results of clusters obtained from the algorithms. A python program has been developed and the program is been trained by applying standard scaler onto a dataset having two features of 200 training sample taken from local retail shop. Both the features are the mean of the amount of shopping by customers and average of the customer's visit into the shop annually. By applying clustering, 5 segments of cluster have been formed labelled as Careless, Careful, Standard,Target and Sensible customers. However, two new clusters emerged on applying mean shift clustering labelled as High buyers and frequent visitors and High buyers and occasional visitors.

关键词： Customer Segmentation k-Means algorithm Mean shift algorithm agglomerative algorithm Machine learning Python

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Study of Preprocessing Sensitivity on Laser Induced Breakdown Spectroscopy (LIBS) Spectral Classification

Study of Preprocessing Sensitivity on Laser Induced Breakdow...

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International Conference on Advances in Computing, Communications and Informatics ICACCI

作者： Sahoo, Tapan Kumar Negi, Atul Gundawar, Manoj Kumar Indian Sch Mines Dept Comp Sci & Engn Dhanbad Bihar India Univ Hyderabad Sch Comp & Informat Sci Hyderabad Andhra Pradesh India Univ Hyderabad Adv Ctr Res High Energy Mat Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479987924

Laser induced breakdown spectroscopy (LIBS) is an atomic emission based spectroscopy that uses a laser pulse as the source of excitation. The laser is focused to form hot plasma, which atomizes and excites the sample. In the LIBS spectrum each "feature" is the amplitude or intensity detected at different wavelengths in the range of 200-1000 nm. Pattern recognition techniques were applied on samples with similar elemental composition resulting in almost similar LIBS spectra which are visually very difficult to differentiate. It was observed that the classification results obtained from different classifiers were sensitive to data preprocessing. The outlier detection and removal techniques PCA, Dendrogram using agglomerative algorithm, Editing by Nearest Neighbour (NN) and Distance Matrix approaches were used in preprocessing step. After removing outlier(s) the resulting training patterns were used to model the k-Nearest Neighbour (k-NN), Principal Component Analysis (PCA), Dendrogram, Multiclass Support Vector Machine (SVM) and Decision Tree classifiers. In k-NN after removing outlier(s) the average classification accuracy was increased by 2% for high energy materials (HEM), but no improvement in non high energy materials (Non HEM) or in top level classification (decide either HEM or Non HEM). But, for other classifiers the classification accuracy gets reduced. Finally instead of removing outlier(s) dimensionality reduction by thresholding was applied and the classification accuracy increased by 4% in k-NN for HEM and 38% in multiclass SVM for HEM and 4% for Non-HEM.

关键词： LIBS PCA dendrogram agglomerative algorithm editing algorithm distance matrix k-NN multiclass SVM decision tree thresholding

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Community Detection in Sample Networks Generated from Gaussian Mixture Model

Community Detection in Sample Networks Generated from Gaussi...

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2nd International Conference on Swarm Intelligence (ICSI)

作者： Zhao, Ling Liu, Tingzhan Liu, Jian Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Commun Univ China Sch Sci Beijing 100024 Peoples R China Peking Univ LMAM Sch Mat Sci Beijing 100871 Peoples R China

ISBN: (纸本)9783642215230

Detecting communities in complex networks is of great importance in sociology, biology and computer science, disciplines where systems are often represented as networks. In this paper, we use the coarse-grained-diffusion-distance based agglomerative algorithm to uncover the community structure exhibited by sample networks generated from Gaussian mixture model, in which the connectivity of the network is induced by a metric. The present algorithm can identify the community structure in a high degree of efficiency and accuracy. An appropriate number of communities can be automatically determined without any prior knowledge about the community structure. The computational results on three artificial networks confirm the capability of the algorithm.

关键词： Community detection Gaussian mixture model Coarse-grained diffusion distance agglomerative algorithm k-means

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GEVA: geometric variability-based approaches for identifying patterns in data

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COMPUTATIONAL STATISTICS 2010年第2期25卷 241-255页

作者： Irigoien, Itziar Arenas, Concepcion Fernandez, Elena Mestres, Francisco Univ Barcelona Fac Biol Dept Estadist E-08028 Barcelona Spain UPV EHU Dept Computat & Artificial Intelligence Donostia San Sebastian Spain Univ Politecn Cataluna Dept Estadist & Invest Operat Barcelona Spain Univ Barcelona Dept Genet E-08028 Barcelona Spain

This paper, arising from population studies, develops clustering algorithms for identifying patterns in data. Based on the concept of geometric variability, we have developed one polythetic-divisive and three agglomerative algorithms. The effectiveness of these procedures is shown by relating them to classical clustering algorithms. They are very general since they do not impose constraints on the type of data, so they are applicable to general (economics, ecological, genetics...) studies. Our major contributions include a rigorous formulation for novel clustering algorithms, and the discovery of new relationship between geometric variability and clustering. Finally, these novel procedures give a theoretical frame with an intuitive interpretation to some classical clustering methods to be applied with any type of data, including mixed data. These approaches are illustrated with real data on Drosophila chromosomal inversions.

关键词： Cluster algorithms Geometric-variability Divisive algorithm agglomerative algorithm Population studies

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An Agglomerate algorithm for Mining Overlapping and Hierarchical Functional Modules in Protein Interaction Networks

An Agglomerate Algorithm for Mining Overlapping and Hierarch...

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6th International Symposium on Bioinformatics Research and Applications

作者： Ren, Jun Wang, Jianxin Chen, Jianaer Li, Min Chen, Gang Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9783642130779

Real PPI networks commonly have large size. Functional modules in them are usually overlapping and hierarchical. So it is significant to identify both overlapping and hierarchical modules with low time complexity. However previous methods can not do it. A new agglomerative algorithm, MOMA, is proposed in the paper to resolve this problem. MOMA classifies subgraphs into clusters and vertices. Clusters can overlap each other. MOMA identifies overlapping and hierarchical functional modules by merging overlapping subgraphs. Its time complexity is O(N-2). We apply MOMA, G-N algorithm and Cfinder on the yeast core PPI network. Comparing with G-N algorithm, MOMA can identify overlapping modules. Comparing with Cfinder, MOMA can identify hierarchical modules. Distributions of the lowest P-value show that the module set identified by MOMA has the stronger biological significance than those identified by the other two algorithms.

关键词： overlapping functional module hierarchical module structure agglomerative algorithm

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Statistical Approach for Community Mining in Social Networks

Statistical Approach for Community Mining in Social Networks

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IEEE International Conference on Service Operations and Logistics and Informatics

作者： Bhatia, M. P. S. Gaur, Pankaj Univ Delhi Dept Comp Engn Netaji Subhas Inst Technol Delhi 110007 India

ISBN: (纸本)9781424420124

The popularity of social networking on the web and the explosive combination with data mining techniques open up vast and so far unexplored opportunities for social intelligence on the web. A network community is a special sub-network that contains a group of nodes sharing similar linked patterns. Many community mining algorithms have been developed in the past. In this work, we have presented a new algorithm BFC (breadth first clustering) which uses statistical approach for community mining in social networks. The algorithm proceeds in breadth first way and incrementally extract communities from the network. This algorithm is simple, fast and can be scaled easily for large social networks. The effectiveness of this approach has been validated using network examples.

关键词： community mining statistical approach agglomerative algorithm

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