Biomedical data presents several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although current research in this field has shown promising results, several research issues still need to be explored. Biomedical data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. The data often suffer from incompleteness, uncertainty and vagueness, which complicates conventional techniques of data mining ranging from the model, algorithm, system and application.
An interesting aspect is to integrate different data sources in the biomedical data analysis process, which requires exploiting the existing domain knowledge from available sources. There is also a need to explore novel data mining methods in the biomedical research to improve predictive performance along with interpretation. In the past, the evolution of research interest has focused on a relatively new area—granular computing GrC , based on technologies such as fuzzy sets and rough sets.
GrC provides a powerful tool for multiple granularity and multiple-view data analysis, which is of vital importance for understanding data driven analysis at different granularity levels. Biomedical data often contain a significant amount of unstructured, uncertain and imprecise data. GrC exhibits some strong capabilities and advantages in intelligent data analysis, pattern recognition, machine learning, and uncertain reasoning for biomedical data.
GrC aims to find a suitable level of granularity of a given problem which can be adjusted according to the degree of fuzziness of the given problem. How to integrate GrC and data mining to combine their advantages is an interesting and important research topic. Data mining based on granular computing in biomedical data analysis is an emerging field which crosses multiple research disciplines and industry domains.
Image matting in the perception granular deep learning
A vast number of real-world problems can be tackled using techniques encompassed in GrC. GDM research explores the advantages, and also challenges, derived from collecting and mining vast amounts of biomedical data. The aims of this Special Issue in Information Sciences are: 1 to present the state-of-the-art research on granular data mining and its application in biomedical data, and 2 to provide a forum for researchers to discuss the latest progress, new research methodologies, and potential research topics.
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- 1. Introduction;
- Granular Computing: Analysis and Design of Intelligent Systems - CRC Press Book?
- Physics for Medical Imaging Applications: 240 (Nato Science Series II:).
- Counterexamples in Topology;
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- Theory and method of granular computing for big data mining.
- Knowledge-Based Clustering: From Data to Information Granules.
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International Journal of Computational Intelligence Systems
Within the structure, K 4 , K 5 , and K 6 are mutually independent and have no relationships between them; the situation is the same for K 1 and K 3. From the IoT big-data array, a large number of such semantic associations of knowledge granules can be scaled into a certain structural pattern to ensure ease of use, ease of application, and ease of comprehension. This type of semantic analysis of the knowledge granules helps executives and knowledge users develop intelligence for BI applications.
Prior to performing large-scale structural analysis, we visualise Fig 5 a sample schema of semantic associations of knowledge granules within a KC in a hierarchical manner. Such semantic associations may form the backbone of the large-scale structural analysis of knowledge granules that have complex semantic relationships. Thus, in the context of analysis and discussion, we propose a larger-scale organisation of knowledge granules in the form of knowledge sub-clusters KSCs in which each sub-cluster consists of a hierarchical structure depicting the semantic associations of the desired knowledge granules for the specific application.
This work analyses the implementation of our proposed KGAC framework through a neuro-fuzzy analytic architecture. Here, we may consider the fuzzy associated data sets for a BI application to analyse and visualise the potential knowledge granules that can participate in effective functional operations of the business [ 38 — 39 ]. We broadly classify the analysis and discussion part into two phases.
Phase 1 includes the structural analysis of large-scale sub-cluster organisations to accommodate the IoT big-data array. Phase 2 highlights the computational analysis used to empirically study the performance of our KGAC framework applications. In this phase, we discuss the structural exploration of large-scale clusters and sub-cluster organisation along with the semantic linkage analysis used to cope with the IoT big-data array. The basic correlation of the knowledge set KS , in which each KC is logically divided into a number of KCs such that each individual cluster is correlated with a KS, is described in Fig 6.
The progressive computer architecture supports the architectural base of the multi-dimensional IoT big-data array for many parallel machines and can be used for large-scale computations and knowledge granule analysis in numerous BI applications. Some analysis has been conducted here to compute the diameter and total KS. The higher-dimensional array of KCs and KSCs organises the knowledge granules in accordance with standard principles and strategies that vary across numerous BI applications.
Knowledge-based clustering :from data to information granules /Witold Pedrycz. – National Library
In Fig 8 , we describe a large-scale semantic linkage analysis of knowledge granules in two KCs. Fig 5 defines the interrelationships of knowledge granules within a sub-cluster. A large-scale extension of Fig 5 is presented in Fig 8 for the purpose of focusing on both the intra and interrelationships among the sub-clusters. By analysing the large-scale associated semantic linkage, standard semantic associations between clusters f and g can be discovered. The equation is written as follows:. Eq 7 can be analysed to obtain the maximum possible number of associations between clusters f and g.
The evolution of computer architecture in a parallel programming platform may efficiently regulate the quick processing of higher- dimensional IoT big-data arrays and thereby contribute to the analytic of knowledge granules.
In this phase, comprehensive computational analysis is used to empirically study the performance of our KGAC framework implementations. A number of computational intelligence approaches, such as the fuzzy approach, neural approach, type-1 neuro-fuzzy approach, and type-2 neuro-fuzzy approach, are available to implement the functional processes of KGAC framework; however, the type-2 neuro-fuzzy analytic approach is more effective in offering greater tolerance and better at addressing the uncertainties actually encountered in BI applications [ 40 — 41 ].
The functional and operational analysis of our KGAC framework is mapped to a neuro-fuzzy analytic architecture to empirically estimate the KAP. In this case analysis, we talk over a business intelligence case that supports to construct a business security strategy of a B2C organisation selling products and services. Consider an example case of a B2C organisation, such as Flipkart. The IoT big data, through coordinated machine learning and integration of different data sources and actions, creates several challenges and opportunities for the progressive e-business scenarios.
We have used the case base analytics of a B2C organisation, where three important parameters affecting the buying behaviour of a consumer are considered. The business security strategy of a B2C organisation depends on the following parameters: service reliability, product quality assurance, trade policies, and buying behaviour. The real-time business data sets can be transformed into fuzzified KSs having a unified configuration in the range of [0, 1] to build the intelligence for a BI application. The KC of a BI application consists of intelligent business parameters service reliability, product quality assurance, trade policy, and buying behaviour along with their possible rules of associations, semantic linkages, and dependability standards to cope with the contemporary trends and prospective BI.
Fig 9 shows the cluster analysis of knowledge granules associated with the intelligence business parameters of a BI application. The graphical analysis indicates several knowledge inferences that are involved with the BI applications. Several other knowledge inferences are also available in fuzzified linguistic terms for the BI application. Precision error is a common hazard in the analytic of knowledge granules; it is known as KAP error.
The neuro-fuzzy structure is capable of extracting the KSs for a multi-rule base system from the BI-statistical business data sets. The neuro-fuzzy environment of MATLAB a also provides the adequate support to implement the neural representations of the GAC-framework using fuzzified data sets to accommodate the fractional values of membership grades to minimise the KAP error [ 42 ].
The neuro-fuzzy algorithm can be used as a standard training and testing algorithm for the KGAC framework that calculates the computed outputs to be matched with the desired target output of the training and testing knowledge granules [ 43 — 44 ]. To implement the KGAC framework, we use the least square estimator function evolving with the Gaussian fuzzy membership grade as a learning mechanism, in which the estimator function is intrinsically non-linear in nature. Now, based on the above parameters, the KAP error analysis results can be computed as described in Table 4.
For the implementation, the number of nodes in hidden layers is considered to be the number of rules. So the effective estimations of errors helps to discover the best fit knowledge cluster that participates in knowledge analytic process so as to generate more precise cognitive decisions and actuations as compared to other poor fit, average fit and good fit knowledge clusters.
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The computations of both approaches are performed stochastically by analysing the BI application data, whose state is computationally analysed in Table 3. In Fig 11 , we analyse the cluster prediction precision outcomes based on the fuzzified input class. We investigate the uncertainties that may arise during cluster prediction, and numerous probabilistic scenarios are considered to empirically estimate the prediction precisions. We obtain an average cluster prediction precision of 0.
We obtain an average cluster prediction error of 0. The degree of uncertainty in the fuzzified input class may affect the predictive outcomes of the proposed KGAC framework. Thus, minimising such errors enables the construction of a robust cluster and sub-cluster organisations of high-dimensional IoT big data for effective knowledge analysis and predictions. We considered the sensitivity analysis scenario of the potential outcomes of the KGAC framework that can be effectively used for the BI service application. In our analysis, we consider that the BI service application data are higher-dimensional IoT big-data arrays and thereby contribute to the transformation of the analytic of knowledge granules into a business goldmine.
Several analyses and explorations that have alliances with the IoT knowledge analytic framework are made. We also estimate the error comparison result by comparison with the standard SCG approach. Thus, based on the needs of the BI applications, the specific KC may be processed for knowledge analytic operations to accomplish various intelligent business tasks, such as planning, forecasting, decision making, and strategy building, for further insights and cognitive actuations. An e-KGC mechanism is discussed for the simple clustering of large-scale knowledge granules.
The semantic association of knowledge granules inside the clusters and sub-clusters helps represent highly multifaceted decisions that can be used by organisations to develop business intelligence for commercial BI applications. We have presented a detailed discussion of the prospective implementation of a type-2 neuro-fuzzy architecture to achieve the desired level of KAP, industrial applicability, tolerance in precision and uncertainty, and overall functional efficiency. Our analysis and discussion also illustrate the feasibility of discovering knowledge granules with the aim of achieving high KAP.
Such a hybrid architecture integrates the good features of neural systems and fuzzy systems with type-2 adaptations to provide higher uncertainty and fault tolerance, better learning ability, better knowledge analytic ability, and better knowledge representation ability compared to a standard fuzzy system by successfully minimising the KAP error. Our framework can help executives and knowledge users generate cognitive decisions, plans, and actuation for the effective monitoring of BI applications.
In the future, we would like to develop a novel re-engineering framework that aims for semantic level knowledge analysis and visualisation-based ontology from IoT big-data arrays for numerous BI applications. The authors also would like to express their deep appreciation to all anonymous reviewers for their kind comments. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U.
Published online Nov Yong Deng, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The authors have declared that no competing interests exist. Received Jun 23; Accepted Oct This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
This article has been cited by other articles in PMC. Abstract The current rapid growth of Internet of Things IoT in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. Introduction An Internet of Things IoT big-data array can be defined as the large-scale organisation of IoT data into certain structural patterns in such a way as to ensure ease of use, ease of application, and ease of comprehension.
Open in a separate window. Fig 1. Associated Studies In this section, we discuss allied studies related to the analysis and clustering of knowledge granules from IoT big-data arrays associated with some BI applications. Fig 2. Layers Responsibility Input layer Syntax and semantics analysis 1st hidden layer Pattern analysis 2nd hidden layer Expert system support Output layer Knowledge granule accumulation Cluster layer Clustering of knowledge granules.
Formulation of a multi-rule based system The multi-rule based system can be used to represent highly multifaceted decisions for numerous BI applications [ 31 — 34 ]. Fig 3. Sequence Rules RC 1. Fig 4. Analytical implementation The implementation of neuro-fuzzy implementation helps improve the KGAC framework so that it can identify and cluster knowledge granules with the maximum fitness values; this clusters the best-fit knowledge granules for the configuration of a multi-rule based system to regulate BI applications.
Box 1. Fig 5. Analysis and Discussion This work analyses the implementation of our proposed KGAC framework through a neuro-fuzzy analytic architecture. Phase 1 Large-scale Structural Analysis In this phase, we discuss the structural exploration of large-scale clusters and sub-cluster organisation along with the semantic linkage analysis used to cope with the IoT big-data array.
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