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Different classical and hybrid intelligent techniques have evolved over the years for the purpose of clustering different datasets. In the machine learning environment, among the different techniques in vogue, we interchangeably used the terms classification, regression and clustering for the inductive learning. The DIKW process of knowledge engineering has been extended and influenced by different intelligent techniques, hybrid in nature. In this article, we propose a hybrid model of Fuzzy C Means with K-nearest neighbour(FCM-KNN) to classify different datasets. In the proposed model, we used one input layer, two middle layers for processing the input files and one output layer for visualization and data collection. We compare the same with Naive Bayes Learning and SVM. Out of three discussed machine learning techniques, Naive Bayes and SVM are supervised in nature and FCM-KNN is an unsupervised classification techniques. Results of the proposed models are demonstrated on a real-life dataset using KNIME model design. A comparative study with the results obtained with the proposed architecture outperforms the other two models, viz. Naive Bayes and SVM, in terms of correct computation percentage. [ABSTRACT FROM AUTHOR]

Clustering techniques find homogeneous and distinguishable prototypes. Careful interpretation of these prototypes is crucial to assist the experts to better organize this know-how and to really improve their decision-making processes. The Traffic Lights Panel was introduced in 2009 as a postprocessing tool to provide understanding of clustering prototypes. In this work, annotated Traffic Lights Panel (aTLP) is presented as an enrichment of the TLP to manage the intrinsic uncertainty related with prototypes themselves. The aTLP handles uncertainty through a quantification of the prototypes' purity based on the variation coefficients (VC) and an associated color-based uncertainty model, with two dimensions - tone and saturation - representing nominal trend and purity of the prototype. An application to a waste-water treatment plant in Slovenia, in a discrete and continuous approach, suggests that aTLP seems a useful and friendly tool able to reduce the gap between data mining and effective decision support, towards informed-decisions. [ABSTRACT FROM AUTHOR]

Knowledge discovery and data mining methods have been successful in many domains. However, their abilities to build or discover a domain theory remain unclear. This is largely due to the fact that many fundamental KDD&DM methodological questions are still unexplored such as (1) the nature of the information contained in input data relative to the domain theory, and (2) the nature of the knowledge that these methods discover. The goal of this paper is to clarify methodological questions of KDD&DM methods. This is done by using the concept of Relational Data Mining (RDM), representative measurement theory, an ontology of a subject domain, a many-sorted empirical system (algebraic structure in the first-order logic), and an ontology of a KDD&DM method. The paper concludes with a review of our RDM approach and 'Discovery' system built on this methodology that can analyze any hypotheses represented in the first-order logic and use any input by representing it in many-sorted empirical system. [ABSTRACT FROM AUTHOR]

Current Business Intelligence (BI) initiatives customize DM-KDD techniques into business analytics, which cannot be used in applications other than business. A review of current methodology at the strategic level of the KM/KDD domains indicates that there exists no general formal framework which can be adopted in new applications, or new application areas. There are no established procedures for the domain expert to express their prior knowledge, understanding and aims in a way which can be linked to KDD/DMM processes and subsequent deployment of discovered knowledge. It is suggested that the sequential life-cycle project-management approach of CRISP-DM needs to be complemented by a dynamic interactive view of a conceptual data/information/knowledge hierarchy in the KM context. It is also suggested that a graphical/visual knowledge representation framework needs to be developed as the basis of a knowledge and discovery and communication framework (KDCF). A review of the limitations in DM methodology at the technical/technological level leads to the conclusion that there is no coherent DM methodology to guide the choice of models and their evaluation, that the DM discipline is fractionated, and that the fundamental search and sampling paradigms have been insufficiently utilized in DM development. It is proposed that development of linked data and model ontologies, together with a DM-epistemology, and associated with full exploitation of search and sampling could lead to improved cohesion and efficacy of the DM discipline. [ABSTRACT FROM AUTHOR]

Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design, and other areas. Neural networks and decision tree methods have serious limitations in capturing relations that may have a variety of forms. Learning systems based on symbolic first-order logic (FOL) representations capture relations naturally. The learned regularities are understandable directly in domain terms that help to build a domain theory. This paper describes relational data mining methodology and develops it further for numeric data such as financial and spatial data. This includes (1) comparing the attribute-value representation with the relational representation, (2) defining a new concept of joint relational representations, (3) a process of their use, and the Discovery algorithm. This methodology handles uniformly the numerical and interval forecasting tasks as well as classification tasks. It is shown that Relational Data Mining (RDM) can handle multiple constrains, initial rules and background knowledge very naturally to reduce the search space in contrast with attribute-based data mining. Theoretical concepts are illustrated with examples from financial and image processing domains. [ABSTRACT FROM AUTHOR]