Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters

Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents

The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the 201C;noise.201D; Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records

Unsupervised and semi-supervised fuzzy clustering with multiple kernels.

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Recently, kernel-based clustering has been proposed to perform clustering in a higher-dimensional feature space spanned by embedding maps and corresponding kernel functions. Although good results were obtained using the Gaussian kernel function, its performance depends on the selection of the scaling parameter among an extensive range of possibilities. This step is often heavily influenced by prior knowledge about the data and by the patterns we expect to discover. Unfortunately, it is often unclear which kernels are more suitable for a particular task. The problem is aggravated for many real-world clustering applications, in which the distributions of the different clusters in the feature space exhibit large variations. Thus, in the absence of a priori knowledge, a single kernel selected from a predefined group is sometimes insufficient to represent the data. One way to learn optimal scaling parameters is through an exhaustive search of one optimal scaling parameter for each cluster. However, this approach is not practical since it is computationally expensive, especially when the data includes a large number of clusters and when the dynamic range of possible values of the scaling parameters is large. Moreover, the evaluation of the resulting partition in order to select the optimal parameters is not an easy task. To overcome the above drawbacks, we introduce two novel fuzzy clustering techniques that use Multiple Kernel Learning to provide an elegant solution for parameter selection. The Fuzzy C-Means with Multiple Kernels algorithm (FCMK) simultaneously finds the optimal partition and the cluster-dependent kernel combination weights that reflect the intrinsic structure of the data. The Relational Fuzzy Clustering with Multiple Kernels (RFCMK) learns the kernel combination weights by optimizing the relational dissimilarities. Consequently, the learned kernel combination weights reflect the relative density, size, and position of each cluster with respect to the other clusters. We also extended FCMK and RFCMK to the semi-supervised paradigms. We show that the incorporation of prior knowledge in the unsupervised clustering task in the form of a small set of constraints on which instances should or should not reside in the same cluster, guides the unsupervised approaches to a better partitioning of the data and avoid local minima, especially for high dimensional real world data. All of the proposed algorithms are optimized iteratively by dynamically updating the partition and the kernel combination weights in each iteration. This makes these algorithms simple and fast. Moreover, our algorithms are formulated to work on both vector and relational data. This makes them applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. We also introduced two relational fuzzy clustering with multiple kernel algorithms for large data to deal with the scalability issue of RFCMK. The random sample and extend RFCMK (rseRFCMK) computes cluster prototypes from a smaller sample of randomly selected objects, and then extends the partition to the remainder of the data. The single pass RFCMK (spRFCMK) sequentially loads manageable sized chunks, clustering the chunks in a single pass, and then combining the results from each chunk. Our extensive experiments show that RFCMK and SS-RFCMK outperform existing algorithms. In particular, we show that when data include clusters with various intrinsic structures and densities, learning kernel weights that vary over clusters is crucial in obtaining a good partition

Unsupervised and semi-supervised clustering with learnable cluster dependent kernels.

Despite the large number of existing clustering methods, clustering remains a challenging task especially when the structure of the data does not correspond to easily separable categories, and when clusters vary in size, density and shape. Existing kernel based approaches allow to adapt a specific similarity measure in order to make the problem easier. Although good results were obtained using the Gaussian kernel function, its performance depends on the selection of the scaling parameter. Moreover, since one global parameter is used for the entire data set, it may not be possible to find one optimal scaling parameter when there are large variations between the distributions of the different clusters in the feature space. One way to learn optimal scaling parameters is through an exhaustive search of one optimal scaling parameter for each cluster. However, this approach is not practical since it is computationally expensive especially when the data includes a large number of clusters and when the dynamic range of possible values of the scaling parameters is large. Moreover, it is not trivial to evaluate the resulting partition in order to select the optimal parameters. To overcome this limitation, we introduce two new fuzzy relational clustering techniques that learn cluster dependent Gaussian kernels. The first algorithm called clustering and Local Scale Learning algorithm (LSL) minimizes one objective function for both the optimal partition and for cluster dependent scaling parameters that reflect the intra-cluster characteristics of the data. The second algorithm, called Fuzzy clustering with Learnable Cluster dependent Kernels (FLeCK) learns the scaling parameters by optimizing both the intra-cluster and the inter-cluster dissimilarities. Consequently, the learned scale parameters reflect the relative density, size, and position of each cluster with respect to the other clusters. We also introduce semi-supervised versions of LSL and FLeCK. These algorithms generate a fuzzy partition of the data and learn the optimal kernel resolution of each cluster simultaneously. We show that the incorporation of a small set of constraints can guide the clustering process to better learn the scaling parameters and the fuzzy memberships in order to obtain a better partition of the data. In particular, we show that the partial supervision is even more useful on real high dimensional data sets where the algorithms are more susceptible to local minima. All of the proposed algorithms are optimized iteratively by dynamically updating the partition and the scaling parameter in each iteration. This makes these algorithms simple and fast. Moreover, our algorithms are formulated to work on relational data. This makes them applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. Our extensive experiments show that FLeCK and SS-FLeCK outperform existing algorithms. In particular, we show that when data include clusters with various inter-cluster and intra-cluster distances, learning cluster dependent kernel is crucial in obtaining a good partition

Parameter-free agglomerative hierarchical clustering to model learners' activity in online discussion forums

L'anàlisi de l'activitat dels estudiants en els fòrums de discussió online implica un problema de modelització altament depenent del context, el qual pot ser plantejat des d'aproximacions tant teòriques com empíriques. Quan aquest problema és abordat des de l'àmbit de la mineria de dades, l'enfocament més comunament adoptat és el de la classificació no supervisada (o clustering), donant lloc, d'aquesta manera, a un escenari de clustering en el qual el nombre real de clústers és a priori desconegut. Per tant, aquesta aproximació revela una qüestió subjacent, la qual no és sinó un dels problemes més coneguts del paradigma del clustering: l'estimació del nombre de clústers, habitualment seleccionat per l'usuari concorde a algun tipus de criteri subjectiu que pot comportar fàcilment l'aparició de biaixos indesitjats en els models obtinguts. Amb l'objectiu d'evitar qualsevol intervenció de l'usuari en l'etapa de clustering, dos nous criteris d'unió entre clústers són proposats en la present tesi, els quals, al seu torn, permeten la implementació d'un nou algorisme de clustering jeràrquic aglomeratiu lliure de paràmetres. Un complet conjunt d'experiments indica que el nou algorisme de clustering és capaç de proporcionar solucions de clustering òptimes enfront d'una gran varietat d'escenaris de clustering, sent capaç de bregar amb diferents classes de dades, així com de millorar el rendiment ofert pels algorismes de clustering més àmpliament emprats en la pràctica. Finalment, una estratègia d'anàlisi de dues etapes basada en el paradigma del clustering subespaial és proposada a fi d'abordar adequadament el problema de la modelització de la participació dels estudiants en les discussions asíncrones. Combinada amb el nou algorisme clustering, l'estratègia proposada demostra ser capaç de limitar la intervenció subjectiva de l'usuari a les etapes d'interpretació del procés d'anàlisi i de donar lloc a una completa modelització de l'activitat duta a terme pels estudiants en els fòrums de discussió online.El análisis de la actividad de los estudiantes en los foros de discusión online acarrea un problema de modelización altamente dependiente del contexto, el cual puede ser planteado desde aproximaciones tanto teóricas como empíricas. Cuando este problema es abordado desde el ámbito de la minería de datos, el enfoque más comúnmente adoptado es el de la clasificación no supervisada (o clustering), dando lugar, de este modo, a un escenario de clustering en el que el número real de clusters es a priori desconocido. Por tanto, esta aproximación revela una cuestión subyacente, la cual no es sino uno de los problemas más conocidos del paradigma del clustering: la estimación del número de clusters, habitualmente seleccionado por el usuario acorde a algún tipo de criterio subjetivo que puede conllevar fácilmente la aparición de sesgos indeseados en los modelos obtenidos. Con el objetivo de evitar cualquier intervención del usuario en la etapa de clustering, dos nuevos criterios de unión entre clusters son propuestos en la presente tesis, los cuales, a su vez, permiten la implementación de un nuevo algoritmo de clustering jerárquico aglomerativo libre de parámetros. Un completo conjunto de experimentos indica que el nuevo algoritmo de clustering es capaz de proporcionar soluciones de clustering óptimas frente a una gran variedad de escenarios de clustering, siendo capaz de lidiar con diferentes clases de datos, así como de mejorar el rendimiento ofrecido por los algoritmos de clustering más ampliamente utilizados en la práctica. Finalmente, una estrategia de análisis de dos etapas basada en el paradigma del clustering subespacial es propuesta a fin de abordar adecuadamente el problema de la modelización de la participación de los estudiantes en las discusiones asíncronas. Combinada con el nuevo algoritmo clustering, la estrategia propuesta demuestra ser capaz de limitar la intervención subjetiva del usuario a las etapas de interpretación del proceso de análisis y de dar lugar a una completa modelización de la actividad llevada a cabo por los estudiantes en los foros de discusión online.The analysis of learners' activity in online discussion forums leads to a highly context-dependent modelling problem, which can be posed from both theoretical and empirical approaches. When this problem is tackled from the data mining field, a clustering-based perspective is usually adopted, thus giving rise to a clustering scenario where the real number of clusters is a priori unknown. Hence, this approach reveals an underlying problem, which is one of the best-known issues of the clustering paradigm: the estimation of the number of clusters, habitually selected by user according to some kind of subjective criterion that may easily lead to the appearance of undesired biases in the obtained models. With the aim of avoiding any user intervention in the cluster analysis stage, two new cluster merging criteria are proposed in the present thesis, which allow to implement a novel parameter-free agglomerative hierarchical algorithm. A complete set of experiments indicate that the new clustering algorithm is able to provide optimal clustering solutions in the face of a great variety of clustering scenarios, both having the ability to deal with different kinds of data and outperforming clustering algorithms most widely used in practice. Finally, a two-stage analysis strategy based on the subspace clustering paradigm is proposed to properly tackle the issue of modelling learners' participation in the asynchronous discussions. In combination with the new clustering algorithm, the proposed strategy proves to be able to limit user's subjective intervention to the interpretation stages of the analysis process and to lead to a complete modelling of the activity performed by learners in online discussion forums

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps

This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster analysis over ubiquitous data streams, where two complementary problems arise: first, to generate (local) SOM models over potentially unbounded multi-dimensional non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments. Towards this problematic, original contributions are made in terms of algorithms and methodologies. Two different methods are proposed regarding the first problem. By focusing on visual knowledge discovery, these methods fill an existing gap in the panorama of current methods for cluster analysis over data streams. Moreover, the original SOM capabilities in performing both clustering of observations and features are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies. Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use. This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future