178,284 research outputs found
An empirical study on the visual cluster validation method with Fastmap
This paper presents an empirical study on the visual method for cluster validation based on the Fastmap projection. The visual cluster validation method attempts to tackle two clustering problems in data mining: to verify partitions of data created by a clustering algorithm; and to identify genuine clusters from data partitions. They are achieved through projecting objects and clusters by Fastmap to the 2D space and visually examining the results by humans. A Monte Carlo evaluation of the visual method was conducted. The validation results of the visual method were compared with the results of two internal statistical cluster validation indices, which shows that the visual method is in consistence with the statistical validation methods. This indicates that the visual cluster validation method is indeed effective and applicable to data mining applications.published_or_final_versio
An Efficient Visual Analysis Method for Cluster Tendency Evaluation, Data Partitioning and Internal Cluster Validation
Visual methods have been extensively studied and performed in cluster data analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as Enhanced-Visual Assessment Tendency (E-VAT) algorithm generally represent D as an n times n image I( overlineD) where the objects are reordered to expose the hidden cluster structure as dark blocks along the diagonal of the image. A major constraint of such methods is their lack of ability to highlight cluster structure when D contains composite shaped datasets. This paper addresses this limitation by proposing an enhanced visual analysis method for cluster tendency assessment, where D is mapped to D' by graph based analysis and then reordered to overlineD' using E-VAT resulting graph based Enhanced Visual Assessment Tendency (GE-VAT). An Enhanced Dark Block Extraction (E-DBE) for automatic determination of the number of clusters in I( overlineD') is then proposed as well as a visual data partitioning method for cluster formation from I( overlineD') based on the disparity between diagonal and off-diagonal blocks using permuted indices of GE-VAT. Cluster validation measures are also performed to evaluate the cluster formation. Extensive experimental results on several complex synthetic, UCI and large real-world data sets are analyzed to validate our algorithm
Image-based activity pattern segmentation using longitudinal data of the German Mobility Panel
In this paper, we present an approach to segment people based on a visualization of the longitudinal week activity data from the German Mobility Panel. In order to perform segmentations, different clustering methods are commonly used. Most of the approaches require comprehensive prior knowledge about the input data, e.g., condensing information to cluster-forming variables. As this may influence the method itself, we used images with a high degree of freedom. These images show week activity schedules of people, including all trips and activities with their purposes, modes as well as their duration or their temporal position within the week. Thus, we answer the question whether using only this type of image data as input will produce reasonable clustering results as well. For the clustering, we extracted the images from an existing tool, processed them for the method and finally used them again to select the final cluster solution based on the visual impression of cluster assignments. Our results are meaningful as we identified seven activity patterns (clusters) using this visual validation. The approach is confirmed by the data-based analysis of the cluster solution showing also interpretable key figures for all patterns. Thus, we show an approach taking into account many aspects of travel behavior as an input to clustering, while ensuring the interpretability of solutions. Usually, key figures from the data are used for validation, but this practice may obscure some aspects of the longitudinal data, which are visible when looking on the images as validation
Which visual questions are difficult to answer? Analysis with Entropy of Answer Distributions
We propose a novel approach to identify the difficulty of visual questions
for Visual Question Answering (VQA) without direct supervision or annotations
to the difficulty. Prior works have considered the diversity of ground-truth
answers of human annotators. In contrast, we analyze the difficulty of visual
questions based on the behavior of multiple different VQA models. We propose to
cluster the entropy values of the predicted answer distributions obtained by
three different models: a baseline method that takes as input images and
questions, and two variants that take as input images only and questions only.
We use a simple k-means to cluster the visual questions of the VQA v2
validation set. Then we use state-of-the-art methods to determine the accuracy
and the entropy of the answer distributions for each cluster. A benefit of the
proposed method is that no annotation of the difficulty is required, because
the accuracy of each cluster reflects the difficulty of visual questions that
belong to it. Our approach can identify clusters of difficult visual questions
that are not answered correctly by state-of-the-art methods. Detailed analysis
on the VQA v2 dataset reveals that 1) all methods show poor performances on the
most difficult cluster (about 10% accuracy), 2) as the cluster difficulty
increases, the answers predicted by the different methods begin to differ, and
3) the values of cluster entropy are highly correlated with the cluster
accuracy. We show that our approach has the advantage of being able to assess
the difficulty of visual questions without ground-truth (i.e. the test set of
VQA v2) by assigning them to one of the clusters. We expect that this can
stimulate the development of novel directions of research and new algorithms.
Clustering results are available online at https://github.com/tttamaki/vqd .Comment: accepted by IEEE access available at
https://doi.org/10.1109/ACCESS.2020.3022063 as "An Entropy Clustering
Approach for Assessing Visual Question Difficulty
How software engineering research aligns with design science: A review
Background: Assessing and communicating software engineering research can be
challenging. Design science is recognized as an appropriate research paradigm
for applied research but is seldom referred to in software engineering.
Applying the design science lens to software engineering research may improve
the assessment and communication of research contributions. Aim: The aim of
this study is 1) to understand whether the design science lens helps summarize
and assess software engineering research contributions, and 2) to characterize
different types of design science contributions in the software engineering
literature. Method: In previous research, we developed a visual abstract
template, summarizing the core constructs of the design science paradigm. In
this study, we use this template in a review of a set of 38 top software
engineering publications to extract and analyze their design science
contributions. Results: We identified five clusters of papers, classifying them
according to their alignment with the design science paradigm. Conclusions: The
design science lens helps emphasize the theoretical contribution of research
output---in terms of technological rules---and reflect on the practical
relevance, novelty, and rigor of the rules proposed by the research.Comment: 32 pages, 10 figure
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more
cameras are mounted on actuated mechanisms such as a gimbal. Existing methods
for DCC calibration rely on joint angle measurements to resolve the
time-varying transformation between the dynamic and static camera. This
information is usually provided by motor encoders, however, joint angle
measurements are not always readily available on off-the-shelf mechanisms. In
this paper, we present an encoderless approach for DCC calibration which
simultaneously estimates the kinematic parameters of the transformation chain
as well as the unknown joint angles. We also demonstrate the integration of an
encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show
the extensions required in order to perform simultaneous online estimation of
the joint angles and vehicle localization state. The proposed calibration
approach is validated both in simulation and on a physical DCC composed of a
2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the
calibrated mechanism integrated into the OKVIS VIO package, and demonstrate
successful online joint angle estimation while maintaining localization
accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201
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