Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study

Validation is one of the most important aspects of clustering, particularly when the user is designing a trustworthy or explainable system. However, most clustering validation approaches require batch calculation. This is an important gap because of the value of clustering in real-time data streaming and other online learning applications. Therefore, interest has grown in providing online alternatives for validation. This paper extends the incremental cluster validity index (iCVI) family by presenting incremental versions of Calinski-Harabasz (iCH), Pakhira-Bandyopadhyay-Maulik (iPBM), WB index (iWB), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP), Representative Cross Entropy (irH), and Conn_Index (iConn_Index). This paper also provides a thorough comparative study of correct, under- and over-partitioning on the behavior of these iCVIs, the Partition Separation (PS) index as well as four recently introduced iCVIs: incremental Xie-Beni (iXB), incremental Davies-Bouldin (iDB), and incremental generalized Dunn\u27s indices 43 and 53 (iGD43 and iGD53). Experiments were carried out using a framework that was designed to be as agnostic as possible to the clustering algorithms. The results on synthetic benchmark data sets showed that while evidence of most under-partitioning cases could be inferred from the behaviors of the majority of these iCVIs, over-partitioning was found to be a more challenging problem, detected by fewer of them. Interestingly, over-partitioning, rather then under-partitioning, was more prominently detected on the real-world data experiments within this study. The expansion of iCVIs provides significant novel opportunities for assessing and interpreting the results of unsupervised lifelong learning in real-time, wherein samples cannot be reprocessed due to memory and/or application constraints

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Proceedings. 16. Workshop Computational Intelligence, Dortmund, 29. Nov.-1. Dez. 2006

These proceedings contain the papers of the 16th Workshop Computational Intelligence. It was organized by the Working Group 5.14 of the VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) and the Working Group Fuzzy-Systems and Soft-Computing of the Gesellschaft für Informatik (GI)

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

Fuzzy Systems

This book presents some recent specialized works of theoretical study in the domain of fuzzy systems. Over eight sections and fifteen chapters, the volume addresses fuzzy systems concepts and promotes them in practical applications in the following thematic areas: fuzzy mathematics, decision making, clustering, adaptive neural fuzzy inference systems, control systems, process monitoring, green infrastructure, and medicine. The studies published in the book develop new theoretical concepts that improve the properties and performances of fuzzy systems. This book is a useful resource for specialists, engineers, professors, and students

Course Manual Winter School on Structure and Functions of Marine Ecosystem: Fisheries

Marine ecosystems comprises of diverse organisms and their ambient abiotic components in varied relationships leading to an ecosystem functioning. These relationships provides the services that are essential for marine organisms to sustain in the nature. The studies examining the structure and functioning of these relationships remains unclear and hence understanding and modelling of the ecological functioning is imperative in the context of the threats different ecosystem components are facing. The relationship between marine population and their environment is complex and is subjected to fluctuations which affects the bottom level of an ecosystem pyramid to higher trophic levels. Understanding the energy flow within the marine ecosystems with the help of primary to secondary producers and secondary consumers are potentially important when assessing such states and changes in these environments. Many of the physiological changes are known to affect the key functional group, ie. the species or group of organisms, which play an important role in the health of the ecosystem. In marine environment, phytoplankton are the main functional forms which serves as the base of marine food web. Any change in the phytoplankton community structure may lead to alteration in the composition, size and structure of the entire ecosystem. Hence, it is critical to understand how these effects may scale up to population, communities, and entire marine ecosystem. Such changes are difficult to predict, particularly when more than one trophic level is affected. The identification and quantification of indicators of changes in ecosystem functioning and the knowledge base generated will provide a suitable way of bridging issues related to a specific ecosystem. New and meaningful indicators, derived from our current understanding of marine ecosystem functioning, can be used for assessing the impact of these changes and can be used as an aid in promoting responsible fisheries in marine ecosystems. Phytoplantkon is an indicator determining the colour of open Ocean. In recent years, new technologies have emerged which involves multidisciplinary activities including biogeochemistry and its dynamics affecting higher trophic levels including fishery. The winter school proposed will provide the insights into background required for such an approach involving teaching the theory, practical, analysis and interpretation techniques in understanding the structure and functioning of marine ecosystems from ground truth measurements as well as from satellite remote sensing data. This is organized with the full funding support from Indian council of Agricultural Research (ICAR) New Delhi and the 25 participants who are attending this programme has been selected after scrutiny of their applications based on their bio-data. The participants are from different States across Indian subcontinent covering north, east, west and south. They are serving as academicians such as Professors/ scientists and in similar posts. The training will be a feather in their career and will enable them to do their academic programmes in a better manner. Selected participants will be scrutinized initially to understand their knowledge level and classes will be oriented based on this. In addition, all of them will be provided with an e-manual based on the classes. All selected participants are provided with their travel and accommodation grants. The faculty include the scientists who developed this technology, those who are practicing it and few user groups who do their research in related areas. The programme is coordinated by the Fishery Resources Assessment Division of CMFRI. This programme will generate a team of elite academicians who can contribute to sustainable management of marine ecosystem and they will further contribute to capacity building in the sector by training many more interested researchers in the years to come

The synthesis of multisensor non-destructive testing of civil engineering structural elements with the use of clustering methods

In the thesis, clustering-based image fusion of multi-sensor non-destructive (NDT) data is studied. Several hard and fuzzy clustering algorithms are analysed and implemented both at the pixel and feature level fusion. Image fusion of ground penetrating radar (GPR) and infrared\ud thermography (IRT) data is applied on concrete specimens with inbuilt artificial defects, as well as on masonry specimens where defects such as plaster delamination and structural cracking were generated through a shear test. We show that on concrete, the GK clustering algorithm exhibits the best performance since it is not limited to the detection of spherical clusters as are the FCM and PFCM algorithms. We also prove that clustering-based fusion outperforms supervised fusion, especially in situations with very limited knowledge about the material properties\ud and depths of the defects. Complementary use of GPR and IRT on multi-leaf masonry walls enabled the detection of the walls’ morphology, texture, as well as plaster delamination\ud and structural cracking. For improved detection of the latter two, we propose using data fusion at the pixel level for data segmentation. In addition to defect detection, the effect of moisture is analysed on masonry using GPR, ultrasonic and complex resistivity tomographies. Within the\ud thesis, clustering is also successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system. Besides, the classification of spectroscopic spatial data from concrete is taken under consideration. In both applications, clustering is used for unsupervised segmentation of data