Clustering Model Research Articles

Explainable multi-omics deep clustering model reveals an important role of DNA methylation in pancreatic ductal adenocarcinoma

Patients with pancreatic ductal adenocarcinoma (PDAC) have the lowest survival rate among all cancer patients in Europe. Since western societies have the highest incidence of pancreatic cancer, it has been projected that PDAC will soon become the second leading cause of cancer-related deaths. The main challenge of PDAC treatment is that patients with similar somatic genotypes exhibit a wide range of disease phenotypes. Artificial Intelligence (AI) is currently transforming the field of healthcare and represents a promising technology for integrating various datasets and optimizing evidence-based decision making. However, the interpretability of most AI models is limited and it is challenging to understand how and why a decision is made. In this study, we developed a deep clustering model for PDAC patient stratification using integrated methylation and gene expression data. We placed a specific emphasis on model explainability, with the aim to understand the hidden patterns learned by the model. The results showed two subgroups of PDAC patients with different prognoses and biological factors. The multi-omics profile analysis revealed the important role of DNA methylation. We also showed how the model was able to learn underlying patterns using both single modalities and their combinations. We hope that this study will help to promote more explainable AI in real-world clinical applications, where the knowledge of decision factors is crucial. The code of this project is publicly available in GitHub (https://github.com/albertolzs/edc_mo_pdac).

Crude oil price forecasting using K-means clustering and LSTM model enhanced by dense-sparse-dense strategy

Crude oil is an essential energy source that affects international trade, transportation, and manufacturing, highlighting its importance to the economy. Its future price prediction affects consumer prices and the energy markets, and it shapes the development of sustainable energy. It is essential for financial planning, economic stability, and investment decisions. However, reaching a reliable future prediction is an open issue because of its high volatility. Furthermore, many state-of-the-art methods utilize signal decomposition techniques, which can lead to increased prediction time. In this paper, a model called K-means-dense-sparse-dense long short-term memory (K-means-DSD-LSTM) is proposed, which has three main training phrases for crude oil price forecasting. In the first phase, the DSD-LSTM model is trained. Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes overfitting. The proposed model is evaluated on two famous crude oil benchmarks: West Texas Intermediate (WTI) and Brent. Empirical evaluations demonstrated the superiority of the DSD-LSTM model over the K-means-LSTM model. Furthermore, the K-means-DSD-LSTM model exhibited even stronger performance. Notably, the proposed method yielded promising results across diverse datasets, achieving competitive performance in comparison to existing methods, even without employing signal decomposition techniques.

Clustering bike sharing stations using Quantum Machine Learning: A case study of Toronto, Canada

Quantum Machine Learning (QML) is a field that combines the principles of Quantum Computing (QC) and Machine Learning (ML). QC works by taking advantage of the properties of quantum physics, such as superposition and entanglement. To fully realize the potential of this technology, more research is necessary, as the field of QML is still in its early stages. Since QC technologies and devices continue to develop quickly, it is important to identify the use cases and applications that benefit the most. This paper investigates the potentials of QC, and more specifically, Quantum Annealing (QA), for clustering real-world data in transportation systems. The Bike Sharing System (BSS) is used as a case study applying a clustering model on QA computers. The main contribution of this research is to introduce a hybrid model to cluster stations in a BSS by solving it as a Constraint Satisfaction Problem (CSP) problem with different methods on a QA computer using a real-time dataset. In addition to the practical contribution, this research also offers theoretical advancements in the field of computational optimization by defining a new topology for the input data that is compatible with QC topology (e.g., Chimera topology). The goal of real-time clustering BSS stations based on dynamic and static datasets is, in fact, to assist decision-makers in better managing and minimizing the risk of bike unavailability at each station and rebalancing bikes shared. Three different methods have been used to determine the number of clusters, and Euclidean, Manhattan, Pearson, and Spearman dissimilarity functions have been applied to cluster the stations. The evaluation is done using the magnitude vs. cardinality approach. The distribution of the stations, magnitude, and cardinality of the results indicate the potential to use QC for clustering for a real-world application, e.g., BSS.

A simple classification and clustering of poverty in rural areas using machine learning

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Predicting ultrasound wave stimulated bone growth in bioinspired scaffolds using machine learning

For conditions like osteoporosis, changes in bone pore geometry even when porosity is constant have been shown to correlate to increased fracture risk using techniques such as dual-energy x-ray absorptiometry (DXA) and computed tomography (CT). Additionally, studies have found that bone pore geometry can be characterized by ultrasound to determine fracture risk, since certain pore geometries can cause stress concentration which in turn will be a source for fracture. However, it is not yet fully understood if changes in pore geometry can be detected by ultrasound when the porosity is constant. Therefore, this study develops an unsupervised machine learning model classifying pore geometry between bioinspired and quadrilateral pore scaffolds with constant porosity using experimental ultrasound wave transmission data. Our results demonstrate that differences in pore geometry can be detected by ultrasound, even at constant porosity, and that these differences can be distinguished in an unsupervised manner with machine learning. For traumatic bone injuries and late-stage osteoporosis where fracture occurs, tissue scaffolds are used to aid the healing of fractures or bone loss. The scaffold design is optimized to match material properties closely with bone, and healing can be enhanced with ultrasound stimulation. In this study we predict the combined effects of ultrasound parameters, such as wave frequency and mode of displacement, and scaffold material properties on bone tissue growth. We therefore develop an unsupervised machine learning clustering model of bone tissue growth in the scaffolds using finite element analysis and bone growth algorithms evaluating effects of pore geometry, scaffold materials, ultrasound wave type and frequency, and mesenchymal stem cell distribution on bone tissue growth. The computational predictions of tissue growth agreed within 10% of comparable experimental studies. The data corresponding to pore geometry, mesenchymal stem cell distribution, and scaffold material demonstrate distinct clusters of total bone formation, while ultrasound frequency and mesenchymal stem cell distribution show distinct clusters in bone growth rate. These variables can be tuned to tailor the scaffold design and optimize the required amount and rate of bone growth to meet a patient's specific needs.

Design of Intelligent Financial System Based on Adaptive Learning Algorithm

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins’ inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub (https://github.com/jvlehtonen/overlap-toolkit).Scientific contributionThis study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.Graphical

PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients' privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient's privacy. Impact Statement-This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy.

Three-fold π delocalisation bonding pattern in bowl-like B70 cluster: an all-boron analogue of the nanographene molecule C48H18

A chemical bonding model for the quasi-planar bowl-like B70 cluster has been presented using the canonical molecular orbital (CMO) and the adaptive natural density partitioning (AdNDP) analyses at the density functional theory (DFT) level. Chemical bonding analysis indicates that the bowl-like B70 is an all-boron analogues of one planar nanographene molecule (C48H18) featuring concentric triple π delocalisation with 6π, 12π and 30π electrons for the inner, middle and outer boron ribbons, respectively. The bonding nature of C48H18 is fully elucidated herein firstly. The infrared spectra of B70 are predicted to facilitate the future experimental characterisations. This research enriches the analogous relationship between planar boron clusters and their polycyclic aromatic hydrocarbons (PAHs) counterparts. The bowl-shaped B70 is the largest boron analogue of PAH reported to date. The unique bonding picture of concentric, three-fold π delocalisation has not been discussed previously in the literature.

Multi-order graph clustering with adaptive node-level weight learning

Current graph clustering methods emphasize individual node and edge connections, while ignoring higher-order organization at the level of motif. Recently, higher-order graph clustering approaches have been designed by motif-based hypergraphs. However, these approaches often suffer from hypergraph fragmentation issue seriously, which degrades the clustering performance greatly. Moreover, real-world graphs usually contain diverse motifs, with nodes participating in multiple motifs. A key challenge is how to achieve precise clustering results by integrating information from multiple motifs at the node level. In this paper, we propose a multi-order graph clustering model (MOGC) to integrate multiple higher-order structures and edge connections at node level. MOGC employs an adaptive weight learning mechanism to automatically adjust the contributions of different motifs for each node. This not only tackles hypergraph fragmentation issue but enhances clustering accuracy. MOGC is efficiently solved by an alternating minimization algorithm. Experiments on seven real-world datasets illustrate the effectiveness of MOGC.

Ensemble Clustering via Co-Association Matrix Self-Enhancement.

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this article, we propose a simple, yet effective CA matrix self-enhancement framework that can improve the CA matrix to achieve better clustering performance. Specifically, we first extract the high-confidence (HC) information from the base clusterings to form a sparse HC matrix. By propagating the highly reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering. Technically, the proposed model is formulated as a symmetric constrained convex optimization problem, which is efficiently solved by an alternating iterative algorithm with convergence and global optimum theoretically guaranteed. Extensive experimental comparisons with 12 state-of-the-art methods on ten benchmark datasets substantiate the effectiveness, flexibility, and efficiency of the proposed model in ensemble clustering. The codes and datasets can be downloaded at https://github.com/Siritao/EC-CMS.

Dynamic Modeling of Clustering in Multimodal Fission of Heavy Nuclei

Dynamic Modeling of Clustering in Multimodal Fission of Heavy Nuclei

Realising Person-Centredness: Transitioning to a Clustered Domestic Model of Aged Residential Care for People With and Without Dementia.

Many older people who cannot live independently live in aged residential care facilities to obtain support with social and healthcare needs. Despite old age being a precious time for people to live well, many facility residents have limited access to activities that promote their well-being and connectedness. In New Zealand, one provider of aged residential care developed a village inspired by de Hogeweyk in the Netherlands, where resident engagement in valued activities supports continuing lifelong identities. The study aimed to explain the transition from a traditional Aged Residential Care facility to a clustered domestic model of care. A critical realist theoretical perspective underpinned case study research. Data comprised transcripts of interviews with key informants, facility staff, residents and their families, records of observation of residents' daily lives, organisational documents, photographs and the first author's study journal. The intersection of philosophical workplace change to support delivery of person-centred care and a change in the physical environment enabled realisation of the organisational vision of residents living normal lives. Policy makers and practitioners must be aware that while a domestic-scale environment provides cues to normal living, staff who know residents and what is important to them enable participation in community and valued activities. Innovative living arrangements are a synthesis of philosophical aspirations, architectural and design vision, dedicated leadership and committed teamwork.

Modeling e-commerce retailer demand using Stacking Ensemble and K-means clustering patterns.

Demand forecasting plays a crucial role in e-commerce. By accurately forecasting product demand, e-commerce companies can better manage inventory levels and decrease excess or out-of-stock situations, thereby reducing inventory costs and increasing customer satisfaction. To address the issue of the traditionally low accuracy in market demand forecasting by practitioners who relied on manual experience, this paper proposed a demand forecasting model for e-commerce retailers based on a Stacking ensemble model. The paper successfully modeled and analyzed past user behavior data from JD.com platform, and the model achieved automated forecasting of future demand based on historical data. Upon testing, the proposed Stacking ensemble model demonstrated RMSE (Root Mean Square Error) is 256.34 and 1-WMAPE is 0.916 close to 1, indicating that the model performed well and was capable of accurately predicting the demand for these products. Additionally, this paper also employed a K-means clustering model based on a cost function to classify products into five distinct categories according to their demand characteristic indicators and product attribute indicators.

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Unsupervised outlier detection using random subspace and subsampling ensembles of Dirichlet process mixtures

Probabilistic mixture models are recognized as effective tools for unsupervised outlier detection owing to their interpretability and global characteristics. Among these, Dirichlet process mixture models stand out as a strong alternative to conventional finite mixture models for both clustering and outlier detection tasks. Unlike finite mixture models, Dirichlet process mixtures are infinite mixture models that automatically determine the number of mixture components based on the data. Despite their advantages, the adoption of Dirichlet process mixture models for unsupervised outlier detection has been limited by challenges related to computational inefficiency and sensitivity to outliers in the construction of outlier detectors. Additionally, Dirichlet process Gaussian mixtures struggle to effectively model non-Gaussian data with discrete or binary features. To address these challenges, we propose a novel outlier detection method that utilizes ensembles of Dirichlet process Gaussian mixtures. This unsupervised algorithm employs random subspace and subsampling ensembles to ensure efficient computation and improve the robustness of the outlier detector. The ensemble approach further improves the suitability of the proposed method for detecting outliers in non-Gaussian data. Furthermore, our method uses variational inference for Dirichlet process mixtures, which ensures both efficient and rapid computation. Empirical analyses using benchmark datasets demonstrate that our method outperforms existing approaches in unsupervised outlier detection.

Multi-view clustering with semantic fusion and contrastive learning

Multi-view data obtained from different sources provides a comprehensive way to model the real world. As one of the fundamental analyses, clustering for multi-view data has attracted increasing attention from researchers in different fields. Although existing multi-view clustering methods have achieved promising results, how to fully utilize the consensus and differences among different views and how to integrate their features to learn a unified representation is still challenging. To overcome the limitations, we put forward a Semantic fusion and Contrastive learning model for Multi-View Clustering (SCMVC) in this paper. SCMVC employs the view-specific encoders to extract embeddings from each view, and reconstructs the topological and attribute information by using the corresponding decoders. Simultaneously, a cross-view contrastive learning module is introduced in SCMVC to assimilate the consensus information across different views. In addition, to distinguish the differences in semantic information across different views, we incorporate an attention mechanism to determine the relative importance of each view in the semantic information fusion. The proposed model is jointly optimized by training and clustering to mutually reinforce each other, gradually improving its performance and robustness. Experiments on three real-world multi-view datasets demonstrate the effectiveness of our SCMVC framework. Source codes are freely available at https://github.com/HexonBang/SCMVC.

Fuzzy Cluster Pitch Synthesis System for the Violin Sound with Machine Learning

Pitch synthesis with violin sound involves the generation of musical pitches using technology to mimic the distinctive tonal characteristics of a violin. This process typically employs digital signal processing techniques to recreate the timbre, articulation, and nuances of a real violin. Advanced algorithms analyze and model the acoustic properties of a violin sound, allowing for the synthesis of realistic pitch variations and expressive qualities. Whether utilized in electronic music production, virtual instruments, or sound design, pitch synthesis with violin sound aims to emulate the rich and complex sonic palette of the violin, offering musicians and composers versatile tools for creative expression and sonic exploration. In this paper proposed Fuzzy Pitch Clustering Machine Learning (FPC-ML) for the violin Music Pitch Synthesis using Machine Learning. The proposed FPC-ML model uses the Fuzzy Clustering model for the estimation of pitches in the violin music signal. Based on the Fuzzy clustering model membership degree is computed for the proposed FPC-ML for the estimation of the pitch in the violin music. With the estimation of linguistic variables, clustering is performed in the Music signal for the computation of pitches. With the estimated pitches in the violin music, the features are trained in the machine learning model for the classification and estimation of features in the Violin Music. Simulation analysis demonstrated that the proposed FPC-ML model computes the features of the Violin Music Pitch values based on the estimated clustering values synthesis performed for the classification of the Violin Music signal. The proposed FPC-ML technique achieves an accuracy value of 0.98 for the violin signal with an iteration of 20. With the increase in several iterations and epoch, the accuracy of the FPC-ML model is further increased for the synthesis of the Violin Music.

Art Therapy to Promote College Students’ Mental Health Based on a Hierarchical Clustering Algorithm

Art therapy is a therapeutic approach that utilizes the creative process of making art to improve mental, emotional, and physical well-being. Art therapy is a form of expressive therapy that utilizes the creative process of making art to improve mental, emotional, and psychological well-being. It provides individuals with a non-verbal outlet for self-expression and exploration, allowing them to communicate and process their thoughts, feelings, and experiences in a safe and supportive environment. This paper proposed an efficient Weighted Hierarchical Clustering Deep Neural Network (WH-CDNN) to promote the mental health of college students. The proposed WH-CDNN model extracts the features of the art therapy to promote the mental health of students. The features considered for the analysis are color palette, texture, and therapy for the promotion of mental health assessment of students. The features associated with the weighted model are computed for the college student mental health assessment. The features with the WH-CDNN model use the hierarchical clustering model for the computation of the features in art therapy based on the assessment of mental health. The examination is based on the consideration of 10 features for the estimation with the 5 clusters for the evaluation of the mental health assessment. Experimental analysis of the results demonstrated that the proposed WH-CDNN model achieves significant improvement in the after the art therapy of the students with the mental health assessment. Through simulation and analysis, the study demonstrates the effectiveness of art therapy in improving mental health outcomes, with significant reductions observed in anxiety and depression levels post-therapy. Moreover, the WH-CDNN model accurately predicts students' mental health states and evaluates the efficacy of art therapy interventions. The findings highlight the potential of integrating advanced computational techniques with art therapy to support student well-being and inform targeted mental health interventions in educational settings.

Generation of Graphic Design Color Schemes Based on CMYK Color Model and Corrosion Algorithms

Graphic design colour schemes play a fundamental role in shaping the visual identity and impact of a design. This paper proposed an efficient Feature Extraction Madhami Fuzzy Clustering Probabilistic Corrosion (FEMFcPC) for the graphic design. The proposed FEMFcPC model comprises the CMYK color scheme for the estimation of the features in the images. With the estimation of the FEMFcPC model corrosion algorithm is implemented for the computation of the graphic design features. Once the features are estimated the related features for the graphic design are designed with the mamdhami fuzzy clustering model. The estimated features are clustered with the CMYK color schemes. The proposed FEMFcPC model estimates the patterns and relationships among graphic designs to provide valuable insights for design categorization and recommendation systems. A dataset comprising various graphic design examples with their corresponding CMYK color schemes is utilized for analysis. Through the application of the FEMFcPC algorithm, clustering results are obtained, revealing distinct grouping patterns and similarities among the designs. The simulation results stated that the proposed FEMFcPC model achieves a significant color scheme for the graphic design. Through the clustering process, the proposed FEMFcPC model with the values of the probabilistic features for the graphic design. The Key findings include the identification of dominant clusters, even distribution of designs across multiple clusters, detection of outliers, and assessment of cluster density. These insights offer significant implications for design categorization, recommendation systems, and targeted marketing strategies within the graphic design domain. The study contributes to advancing our understanding of graphic design analysis and provides a foundation for future research in this field.