聚类是根据数据特征对数据进行划分的技术。性质相似的数据属于同一个簇[1]。评估聚类质量有两类评价方法：一类是外部评估，即数据集中真实标签事先已知；另一类是内部评估，即在没有真实标签的情况下仅利用数据集本身进行评价。本文对IRIS数据集的聚类结果同时进行了外部评估和内部评估。在外部评估中，计算了该数据集的同质性（Homogeneity）、正确性（Correctness）和V-measure分数。在内部性能度量方面，使用了轮廓系数（Silhouette Index）和误差平方和（Sum of Square Errors）。这些内部性能度量与树状图（层次聚类中的图形工具）一起，首先用于验证聚类的簇数。最后，作为一种统计工具，我们使用频率分布方法来比较聚类结果与原始数据中观测值的分布，并提供可视化表示。
英文摘要：
Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to the same cluster [1]. There are two types of evaluation methods to evaluate clustering quality. One is an external evaluation where the truth labels in the data sets are known in advance and the other is internal evaluation in which the evaluation is done with data set itself without true labels. In this paper, both external evaluation and internal evaluation are performed on the cluster results of the IRIS dataset. In the case of external evaluation Homogeneity, Correctness and V-measure scores are calculated for the dataset. For internal performance measures, the Silhouette Index and Sum of Square Errors are used. These internal performance measures along with the dendrogram (graphical tool from hierarchical Clustering) are used first to validate the number of clusters. Finally, as a statistical tool, we used the frequency distribution method to compare and provide a visual representation of the distribution of observations within a clustering result and the original data.
A clustering is an implicit assignment of labels of points, based on proximity to other points. It is these labels that are then used for downstream analysis (either focusing on individual clusters, or identifying representatives of clusters and so on). Thus, in order to trust a clustering as a first step in exploratory data analysis, we must trust the labels assigned to individual data. Without supervision, how can we validate this assignment? In this paper, we present a method to attach affinity scores to the implicit labels of individual points in a clustering. The affinity scores capture t…
Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；BCAM - Basque Center for Applied Mathematics（巴斯克应用数学中心）
Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients. Despite advances in machine learning and IoT, small, nonstigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not yet available. Part of the reason is the complexity of epilepsy itself, including highly imbalanced data, multimodal nature, and very subject-specific signatures. However, another problem is the heterogeneity of methodological approaches in research, leading to slower progress, …
University of Cambridge（剑桥大学）；Lancaster University（兰卡斯特大学）
Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work, we first propose a novel spectral-based subspace clustering algorithm that seeks to represent each point as a sparse convex combination of a few nearby points. We then extend the algorithm to constrained clustering and active learning settings. Our motivation for developing such a framework stems from the fact that typically either a small amount of labelled data is available in advance; or it is possible to label …
School of Computing and Information Systems, University of Melbourne（墨尔本大学计算与信息系统学院）
Cluster analysis is used to explore structure in unlabeled data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computationally obtained clusters. A large number of different internal indices have been developed for validation in the offline setting. However, this concept has not been extended to the online setting. A key challenge is to find an efficient incremental formulation of an index that can capture both cohesion and separation of the clusters over potentially infinite data streams. In this paper, we develop two online versions (…
Queensland University of Technology（昆士兰科技大学）；The University of Queensland（昆士兰大学）
The increasing quantity of PV generation connected to distribution networks is creating challenges in maintaining and controlling voltages in those distribution networks. Determining the maximum hosting capacity for new PV installations based on the historical data is an essential task for distribution networks. Analyzing all historical data in large distribution networks is impractical. Therefore, this paper focuses on how to time efficiently identify the critical cases for evaluating the voltage impacts of the new large PV applications in medium voltage (MV) distribution networks. A systemat…
We address the problem of validating the ouput of clustering algorithms. Given data $\mathcal{D}$ and a partition $\mathcal{C}$ of these data into $K$ clusters, when can we say that the clusters obtained are correct or meaningful for the data? This paper introduces a paradigm in which a clustering $\mathcal{C}$ is considered meaningful if it is good with respect to a loss function such as the K-means distortion, and stable, i.e. the only good clustering up to small perturbations. Furthermore, we present a generic method to obtain post-inference guarantees of near-optimality and stability for a…
Islamic Azad University（伊斯兰阿扎德大学）；Yonsei University（延世大学）
The banking industry is very important for an economic cycle of each country and provides some quality of services for us. With the advancement in technology and rapidly increasing of the complexity of the business environment, it has become more competitive than the past so that efficiency analysis in the banking industry attracts much attention in recent years. From many aspects, such analyses at the branch level are more desirable. Evaluating the branch performance with the purpose of eliminating deficiency can be a crucial issue for branch managers to measure branch efficiency. This work n…
Santa Clara University（圣克拉拉大学）；Florida State University（佛罗里达州立大学）
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. However, methods for evaluating clusterability vary radically, making it challenging to select a suitable measure. In this paper, we perform an extensive comparison of measures of clusterability and provide guidelines that clustering users can reference to select su…
National Technological University of Athens（雅典国家技术大学）
In this paper, we scrutinize the effectiveness of various clustering techniques, investigating their applicability in Cultural Heritage monitoring applications. In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images. A total of 6 different clustering approaches have been evaluated over a set of 14 different orthorectified hyperspectral images. Experimental setup in this study involves K-means, Spectral, Meanshift, DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate i…
Evaluating Neural Networks for Early Maritime Threat Detection
Lake Highland Preparatory School（高地湖预备学校）；University of Central Florida（中佛罗里达大学）；Florida International University（佛罗里达国际大学）；Texas State University（德克萨斯州立大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Seoul National University（首尔国立大学）；Qatar Computing Research Institute, Hamad Bin Khalifa University（哈马德·本·哈利法大学卡塔尔计算研究所）
We address the lack of reliability in benchmarking clustering techniques based on labeled datasets. A standard scheme in external clustering validation is to use class labels as ground truth clusters, based on the assumption that each class forms a single, clearly separated cluster. However, as such cluster-label matching (CLM) assumption often breaks, the lack of conducting a sanity check for the CLM of benchmark datasets casts doubt on the validity of external validations. Still, evaluating the degree of CLM is challenging. For example, internal clustering validation measures can be used to …
From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI
Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫研究所（制造工程与自动化））；Institute of Industrial Manufacturing and Management IFF, University of Stuttgart（工业制造与管理研究所，斯图加特大学）；TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）
The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-known that the major SARS-CoV-2 lineages are characterized by mutations that happen predominantly in the spike protein. Understanding the spike protein structure and how it can be perturbed is vital for understanding and determining if a lineage is of concern. These are crucial to identifying and controlling current outbreaks and preventing future pandemics. Machine learning (ML) methods are a viable solution to this …
