Mining event logs for knowledge discovery based on adaptive efficient fuzzy Kohonen clustering network

Mapping adaptive fuzzy Kohonen clustering network onto distributed image processing system

Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design

Software components analysis is a critical technique for software development and maintenance. Clustering techniques have been widely used in grouping related software components. However, software is complex, but clustering techniques used in software engineering typically adopt only one metric to measure the similarity of components. This paper proposes an adaptive fuzzy clustering technique based on possibilistic clustering algorithms to address the issue of single metric. The proposed technique collaboratively considers distance, density, and the trend of density change of component instances in the membership degree calculation. The post clustering separation of clustered components based on the predefined thresholds and regrouping of the separated component points result in higher cohesive clustering. The proposed algorithm has been evaluated via experiments using a network protocol RSVP-TE system. The comparison of Hierarchical, Self-organizing map (SOM), and fuzzy c-means (FCM) against adaptive fuzzy clustering proposed in this paper indicates that the adaptive fuzzy clustering group software component instances into more cohesive clusters while it is also insensitive to parameter settings.

A fuzzy self-tuning controller based on fuzzy clustering algorithmis proposed in the paper. First of all, the adaptive fuzzy clustering algorithm is presented to confirm the structure of fuzzy model. The Kalman filtering method is used to confirm parameters of fuzzy model. On the basis of the proposed method, the parameters of the controller can be automatically adjusted. To demonstrate the performance of the proposed method, simulation on the nonlinear system is performed. The results show that this method provides effective and accurate control.

In reverse engineering one often needs to generate geometrical models of old mechanical parts. Manual redrafting of old products using computer aided design (CAD) software is a laborious procedure. To overcome this problem, an automated procedure is proposed. The proposed approach is based on using clustering to segment the range image of the object and then constructing a solid model using an automated procedure. To demonstrate the feasibility of the proposed method, the model generation for convex planar faceted objects is considered. A generalized version of the adaptive fuzzy c-elliptotype clustering algorithm suitable for higher dimensional data is used to identify the planar facets in noisy range images when the number of facets is unknown.

Clustering plays a vital role in process mining as it organizes complex event logs into meaningful groups, helping to identify common patterns, outliers, and inefficiencies. This simplification enables organizations to detect bottlenecks and optimize workflows by uncovering trends and variations that might otherwise remain hidden. Fuzzy clustering addresses the challenge of overlapping behaviors, providing actionable insights for targeted improvements and enhanced operational efficiency. Nevertheless, conventional clustering algorithms for process mining focus either on activity sequences or cycle times, resulting in incomplete insights due to the neglect of temporal or structural variations. This work introduces a new fuzzy clustering methodology that incorporates both activity sequences and cycle times through a weighted distance metric. The proposed approach balances the weights of similarity in sequences as well as time variation flexibly using the parameter α, enabling clusters to represent both structural as well as performance-based process attributes. Through using fuzzy C-means clustering, the method allows cases to have multiple memberships with different membership degrees, providing flexibility regarding overlapping process behavior. An experimental evaluation using real-life event logs demonstrates the effectiveness of the method in discerning process variants. It yields superior results compared to conventional methods that account for only sequence-based clustering scenarios, as well as time-based clustering methods. The results describe the significant importance of optimizing clustering results by varying α, where a balanced weighting (α=0.5) gives more meaningful clusters. Ultimately, the framework enhances process mining by offering detailed insights for analyzing operational inefficiencies, bottlenecks, and resource allocation mismatches, providing substantial real-world benefits for industries that demand effective process improvement.

PurposeTarget costing determines the cost of a building before design. The cost of the product is based on the customer's requirements for the product's performance and the customer's willingness to pay for such performance. However, if the designers cannot achieve the target cost, the benefits of target costing are wasted. The purpose of this paper is to describe design steering, a methodology for managing design process to achieve target cost and purposed value for the customer.Design/methodology/approachThe design steering concept steers the complex design process by knowledge management and rapid cost feedback loops, especially in the very early stages of design. Traditional estimating together with CAD systems cannot produce feedback because of the cumulative nature of design. This paper introduces advanced building information modelling (BIM) that supports project management in cost and quality management. As CAD systems start from zero, this BIM starts from the full content. The BIM constructs all the quantities and costs of the building components before the design starts, basing on the client's needs. The model acts as a “defending champion” in relation to the designer's proposals during the design.FindingsDesign steering concept is already in commercial use with encouraging results. A case study explains how design steering affects to designers' decision making. Design steering has helped mutual understanding between the designers and management enabling to achieve the target cost.Research limitations/implicationsAt the moment more transparency on goals, processes and information models of clients, project managers and designers are needed in order to support the commitment process of the participants.Originality/valueDesign process has not been earlier dealt with from top to bottom, but from bottom to top. New BIM technologies enable both perspectives.

An integrated decision support system for building asset management based on BIM and Work Breakdown Structure

Integrated representation of technical systems with BIM and linked data: TUBES system ontology

Statistical as well as adaptive clustering approaches are being currently used for both segmentation and vector quantization of medical images. However, a comparative evaluation of both approaches has rarely been done to identify the efficacy of such approaches to specific applications, for example, image segmentation and vector quantization. The rate distortion functions of three clustering algorithms, namely, the statistical based deterministic annealing, the adaptive fuzzy leader clustering algorithm, and LBG, have been computed for vector quantization using multi-scale vectors in the wavelet domain. Such comparative evaluation serves as a guide for proper selection of clustering algorithms for global codebook generation in vector quantization and for image segmentation.

The silicon content of molten iron is a key index to reveal the thermal state in the blast furnace (BF). To improve the quality of molten iron, it is important to obtain silicon content online accurately. However, owing to the harsh environment in BF hearth, it is difficult to realize the online measurement of the silicon content of molten iron. To handle this problem, a novel soft sensoring method based on adaptive stacked polymorphic model is proposed. First, an adaptive uncertain fuzzy clustering algorithm (AUFC) is utilized to obtain the distribution characteristics and reduce the complexity of the process data, thereby the training samples are divided into several sub-classes. Second, the adaptive weight echo state network (AW-ESN) and time difference AW-ESN (TD-AWESN) are proposed to establish polymorphic models. Inspired by the stacking strategy, sub-models based on stacked polymorphic model are designed, which can effectively solve the time-varying and the strong nonlinear problems in the BF ironmaking process. Third, to avoid model degradation caused by the changes of working conditions, an ensemble strategy is adopted to combine the predicted results of multiple sub-models. The experimental results in an industrial BF show that the soft sensor based on an adaptive polymorphic model can reach the required prediction accuracy and provide reliable silicon content.

The designs of solar energy systems depend mainly on the solar radiation that reaches the earth's surface, as it is difficult to determine the amount of solar radiation precisely due to several climatic, geographical and temporal factors. Therefore, forecasting of solar radiation is necessary before using solar energy systems. In this paper, the researchers present an accuracy MAMDANI fuzzy inference system for solar radiation prediction with meteorological data. This system is based with a two-stage method for Fuzzy Rules Learning through Clustering (FRLC). In the first stage, the subtractive clustering is used to extract the fuzzy rules, the second stage is a linguistic approximation and a refinement of the learned solutions with linguistic hedges. FRLC is compared to multilayer feed-forward neural network and support vector regression. The results of the experiments show the efficacy of linguistic fuzzy rules in the forecasting of solar radiation. In parallel with the prediction, the model provides a good balance between interpretability and accuracy.

Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data point as compared to its surrounding neighbors, and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. It uses the fuzzy rules, based on aforementioned assumption for the selection of cluster centers. We performed a number of experiments on nine synthetic clustering datasets and compared the resulting clusters with the state-of-the-art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.

Wireless Sensor Networks are vital for applications like environmental monitoring, smart cities, and industrial Internet of Things, yet their efficiency is often limited by sensor node energy constraints. To address this, we propose a novel hybrid routing algorithm namely, Fuzzy C-Means - Fuzzy-Based Spotted Hyena Optimization - Quantum Annealing, which enhances energy efficiency and network longevity. The three-phase approach begins with adaptive fuzzy clustering, allowing nodes to belong to multiple clusters for improved load balancing. Next, the Fuzzy-Based Spotted Hyena Optimization algorithm selects initial routes using parameters such as residual energy, distance to the base station, and link quality. Finally, Quantum Annealing refines routing paths to ensure global optimization and avoid local optima. This hybrid method adapts dynamically to changing network conditions and energy levels, delivering superior scalability and performance. Experimental results show a 30% reduction in total energy consumption and a 25% increase in effective data transmission. The algorithm achieves a packet delivery ratio of 98.87%, throughput of 65 kbps, and latency of 115 ms, outperforming conventional techniques. This work contributes a robust and adaptive routing solution, supporting sustainable and efficient WSN deployments in dynamic environments.

An improved detection of blind image forgery using hybrid deep belief network and adaptive fuzzy clustering