scholarly journals Interactive Clustering

2020 ◽  
Vol 53 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Juhee Bae ◽  
Tove Helldin ◽  
Maria Riveiro ◽  
Sławomir Nowaczyk ◽  
Mohamed-Rafik Bouguelia ◽  
...  
Keyword(s):  
Interactive Clustering

I-TWEC: Interactive clustering tool for Twitter

2018 ◽  
Vol 96 ◽  
pp. 1-13 ◽  
Author(s):  
İnanç Arın ◽  
Mert Kemal Erpam ◽  
Yücel Saygın
Keyword(s):  
Interactive Clustering

scholarly journals Interactive clustering

1982 ◽  
Vol 14 (2) ◽  
pp. 170-175 ◽  
Author(s):  
C. P. Whaley
Keyword(s):  
Interactive Clustering

scholarly journals Features Determination from Super-Voxels Obtained with Relative Linear Interactive Clustering

2016 ◽  
Vol 21 (3) ◽  
pp. 69-79 ◽  
Author(s):  
Abdelkhalek Bakkari ◽  
Anna Fabijańska
Keyword(s):  
Computational Cost ◽  
Brain Images ◽  
3D Images ◽  
New Approach ◽  
Magnetic Resonance Imaging Mri ◽  
Ct Brain ◽  
Interactive Clustering ◽  
The Brain ◽  
Low Computational Cost ◽  
3D Magnetic Resonance Imaging

Abstract In this paper, the problem of segmentation of 3D Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) brain images is considered. A supervoxel-based segmentation is regarded. In particular, a new approach called Relative Linear Interactive Clustering (RLIC) is introduced. The method, dedicated to image division into super-voxels, is an extension of the Simple Linear Interactive Clustering (SLIC) super-pixels algorithm. During RLIC execution firstly, the cluster centres and the regular grid size are initialized. These are next clustered by Fuzzy C-Means algorithm. Then, the extraction of the super-voxels statistical features is performed. The method contributes with 3D images and serves fully volumetric image segmentation. Five cases are tested demonstrating that our Relative Linear Interactive Clustering (RLIC) is apt to handle huge size of images with a significant accuracy and a low computational cost. The results of applying the suggested method to segmentation of the brain tumour are exposed and discussed.

scholarly journals Interactive visual exploration and analysis of origin-destination data

2018 ◽  
Vol 1 ◽  
pp. 1-5
Author(s):  
Linfang Ding ◽  
Liqiu Meng ◽  
Jian Yang ◽  
Jukka M. Krisp
Keyword(s):  
Visual Analytics ◽  
Spatial Interaction ◽  
Time Windows ◽  
Visual Exploration ◽  
Interaction Patterns ◽  
Web Based ◽  
Distance Threshold ◽  
Interactive Environment ◽  
Input Variables ◽  
Interactive Clustering

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.

Learning Similarity Matrix from Constraints of Relational Neighbors

2010 ◽  
Vol 14 (4) ◽  
pp. 402-407 ◽  
Author(s):  
Masayuki Okabe ◽  
◽  
Seiji Yamada ◽  
Keyword(s):  
Semidefinite Programming ◽  
Optimization Problem ◽  
User Feedback ◽  
Similarity Matrix ◽  
Pairwise Constraints ◽  
Interactive Clustering ◽  
Additional Constraints

This paper describes a method of learning similarity matrix from pairwise constraints assumed used under the situation such as interactive clustering, where we can expect little user feedback. With the small number of pairwise constraints used, our method attempts to use additional constraints induced by the affinity relationship between constrained data and their neighbors. The similarity matrix is learned by solving an optimization problem formalized as semidefinite programming. Additional constraints are used as complementary in the optimization problem. Results of experiments confirmed the effectiveness of our proposed method in several clustering tasks and that our method is a promising approach.

Incorporating Constraints in System Modularization by Interactive Clustering of Design Structure Matrices

2016 ◽  
Author(s):  
Roozbeh Sanaei ◽  
Kevin N. Otto ◽  
Katja Hölttä-Otto ◽  
Kristin L. Wood
Keyword(s):  
Complex Systems ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Design Structure Matrix ◽  
Complex Products ◽  
Pressure Fields ◽  
Design Structure ◽  
Computational Capability ◽  
New Type ◽  
Interactive Clustering

Modularity is an approach to manage the design of complex systems by partitioning and assigning elements of a concept to simpler subsystems according to a planned architecture. Functional-flow heuristics suggest possible modules that have been demonstrated in past products, but using them still leaves it to the designer to choose which heuristics make sense in a certain architecture. This constitutes an opportunity for a designer to take other constraints and objectives into account. With large complex systems, the number of alternative groupings of elements into modular chunks becomes exponentially large and some form of automation would be beneficial to accomplish this task. Clustering algorithms using the design structure matrix (DSM) representation search the space of alternative relative positioning of elements and present one ideal outcome ordering which “optimizes” a modularity metric. Beyond the problems of lack of interactive exploration around the optimized result, such approaches also partition the elements in an unconstrained manner. Yet, typical complex products are subject to constraints which invalidate the unconstrained optimization. Such architectural partitioning constraints include those associated with external force fields including electric, magnetic, or pressure fields that constrain some functions to perform or not perform in different regions of the field. There are also supplier constraints where some components cannot be easily provided with others. Overall, it is difficult to simply embed all objectives of modular thinking into one metric to optimize. We develop a new type of interactive clustering algorithm approach considering multiple objectives and partitioning constraints. Partitioning options are offered to a designer interactively as a sequence of clustering choices between elements in the architecture. A designer can incorporate constraints that determine the compatibility or incompatibility of elements by choosing among alternative groupings progressively. Our aim is to combine computational capability of clustering algorithms with the flexibility of manual approaches. Through applying these algorithms to a MRI machine injector, we demonstrate the benefits of interactive cooperation between a designer and modularity algorithms, where constraints can be naturally considered.

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