Conventional Grouping Research Articles

Energy saving computation offloading for dynamic CR-MEC systems with NOMA via decomposition based soft actor–critic

This paper investigates resource allocation in cognitive radio (CR) based mobile edge computing (MEC) systems under time-varying channels while considering non-orthogonal multiple access (NOMA) technique. In contrast to prior research on NOMA-MEC, we introduce a generalized user grouping scheme, which makes the secondary users (SUs) have the flexibility to participate in multiple NOMA groups simultaneously, enabling the partial offloading of their task data to multiple MEC servers. To tackle the non-convex long-term energy minimization problem, we propose a decomposition based soft actor–critic (DB-SAC) approach combining conventional convex optimization with deep reinforcement learning (DRL) algorithms, which divides the problem into a joint communication and computation resource allocation subproblem and an offloading ratios optimization subproblem. The former one is further divided into a series of local computation resource and offloading energy minimization problems, addressed through theoretical analysis and convex optimization techniques. For the latter subproblem, we adopt the SAC algorithm to dynamically allocate offloading ratios for SUs. Simulation results showcase that when compared to the conventional non-generalized user grouping scheme, the generalized user grouping scheme can achieve remarkable energy savings of up to 87.5%.

Blood Grouping by Seed Extract: An Innovative Approach in Forensic Science

Background: Blood grouping is a fundamental technique in forensic science, aiding in the identification of individuals involved in criminal investigations and accidents. This research paper introduces a pioneering method that employs plant seed extracts for blood grouping, aiming to revolutionize the field by providing a novel and cost-effective approach. The context and purpose of the study revolve around addressing the limitations of traditional blood grouping methods and exploring the feasibility of utilizing natural compounds present in seeds to determine blood types accurately. The study's primary focus was to investigate the potential of various seed extracts to agglutinate or inhibit the agglutination of blood samples. By conducting extensive experiments and analyses, the research uncovered significant findings. Certain seed extracts exhibited remarkable specificity in agglutinating with distinct blood types, mimicking the reaction patterns seen in conventional blood grouping systems. Conversely, other seed extracts demonstrated inhibitory effects on agglutination, further enhancing the discriminatory power of the approach. This research thus establishes a clear correlation between seed extracts and blood types, demonstrating their potential for accurate blood grouping. Results: In summary, this paper presents a groundbreaking advancement in forensic science by proposing a new perspective on blood grouping using natural seed extracts. The technique's simplicity and cost-effectiveness make it particularly appealing for implementation in resource-constrained settings. Additionally, the ability to utilize common plant seeds found in various geographical regions enhances its practicality and accessibility. Furthermore, the results suggest that this innovative approach has the potential to complement existing blood grouping methods, providing an additional tool for forensic experts to employ. Conclusions: The implications of this research are substantial for both forensic science and criminal investigations. The proposed seed extract-based blood grouping method could substantially expedite the identification process, aiding law enforcement agencies and forensic practitioners. Moreover, its applicability in situations where traditional methods are unavailable or impractical, such as in remote locations or developing countries, underscores its significance. Overall, this study not only introduces a cutting-edge technique for blood grouping but also paves the way for future research avenues in exploring the potential of natural compounds in forensic science applications. In conclusion, this research marks a significant step forward in the realm of blood grouping techniques. The study's findings open up new possibilities for innovative approaches in forensic science and offer promising avenues for enhancing accuracy, efficiency, and accessibility in the field of blood type determination.

Beyond MIPI: Harnessing Machine Learning and Histological Subtype for Enhanced MCL Prognostication of Survival

Introduction Mantle cell lymphoma (MCL) presents challenges due to its aggressive nature and dismal treatment outcomes. The Mantle Cell Lymphoma International Prognostic Index (MIPI) score is valuable for risk stratification, but it may not fully capture the complexity of MCL biology. To address this, we explored innovative data-driven methods based on machine learning to augment the MIPI score. By integrating clinical and histological data with advanced algorithms, this study aimed to enhance risk assessment in MCL. Improved prognostication of survival could facilitate the design of risk-tailored therapeutic approaches, directing more aggressive treatments to high-risk patients, ultimately leading to personalized and precise management. Methods The following patient-level variables were retrieved: age, gender, Ann-Arbor stage (I-II vs III-IV), Bulky disease, extranodal disease, histopathological variant (classic vs pleomorphic/blastoid lymphoma), and Ki67+ cells. The MIPI score was used to categorize patients into low, intermediate, and high-risk groups. Employing random survival forests, we developed a novel risk score by integrating clinical and histological data. Missing variables were imputed using a random forests algorithm. Harrel's concordance indexes (c-indexes) were used to evaluate model's accuracy. Results We assembled a real-world database of 231 MCL patients from 6 Spanish tertiary hospitals (Jan 2000-Dec 2021), and another cohort for validation from another Spanish tertiary hospital (N=44). Median age at diagnosis was 64 years in both cohorts. Median follow-up was 6.28 y and 8.67 y in the training and test sets, and median overall survival (OS) was 11.48 y and 5.61 y. According to the MIPI score, 44% and 38% of patients were of high risk in the training and validation sets; 26% and 39% were of intermediate risk, and 30% & 23% were of low risk. The c-index of the MIPI groups for the prediction of OS was 0.716 and 0.684 in the training and validation sets, respectively. Then, a random survival forest algorithm was used to derive a new risk score in the training set. The first model contained all the available variables mentioned previously. This 8-variable model achieved a cross-validated c-index of 0.764 in the training set. A variable reduction technique was implemented to remove those variables that provided less independent prognostic value. A simplified model was developed, which contained the following variables by order of importance: histological subtype, MIPI-derived groups, age at diagnosis and Ann-Arbor stage. This model outperformed the conventional MIPI grouping strategy, achieving c-indexes of 0.764 and 0.775 in the training and validation sets, respectively ( Figure 1). Conclusion This study showcases the potential of a novel machine learning model in MCL prognostication beyond the traditional MIPI score, utilizing easily accessible clinical information. The inclusion of histological subtype, MIPI score, age at diagnosis, and Ann-Arbor stage as robust independent prognostic factors improves risk prediction accuracy. Future research may further refine the model and explore additional variables to continue optimizing prognostication and treatment strategies for MCL patients.

QoE-aware NOMA user grouping in 5G mobile communications using a multi-stage interval type-2 fuzzy set model

The user grouping has a significant impact on the performance of non-orthogonal multiple access (NOMA) systems. The present work is focused on downlink NOMA user grouping leveraging the type-2 fuzzy set (T2FS). The main drawback of conventional user grouping methods is low efficiency for middle users which degrades the overall system performance. To overcome this problem, a 2-step user grouping process is proposed that includes identifying group candidates and selecting the most qualified one. The first step is handled by introducing a group membership competency criterion based on T2FS modeling due to its capability to handle extra uncertainty in real-world phenomena. The second step involves adding the most qualified candidate to the desired group, given the additional power imposed on the group. The key contribution of the paper is twofold: (i) it affords a multi-stage structure to evaluate the competency of users to join a particular group relying on T2FS modeling. It allows groups of different sizes to be formed, depending on the network channel status and quality of experience (QoE) requirements, and (ii) the additional power imposed on the group is exploited as a measure to select the final group. In this regard, the analysis of interference that each user brings to others in the same group is taken into account. The performance of the proposed scheme is evaluated by simulations and compared with other methods. The obtained results indicate the efficiency of the proposed approach in terms of total power consumption and symbol error rate.

Video-based pedestrian grouping model considering long-span space in a big hall

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.

Management of a Patient with Bombay Blood Group and Chronic Liver Disease with Subdural Hematoma

AbstractThe Bombay blood group is an extremely rare entity within the conventional ABO blood grouping system. End-stage liver disease also presents with myriad disorders of coagulation due to impaired synthesis and dysfunction of clotting factors, which predisposes patients to spontaneous and life-threatening episodes of bleeding. We report a patient with Bombay blood group and end-stage liver disease who presented to our hospital with a spontaneous subdural hematoma. Although conventional parameters of coagulation in this patient were abnormal, we were able to safely defer product transfusion because his thromboelastography (TEG) report was within acceptable ranges. In this article, we discuss our strategy for optimization of extremely limited blood resources in this scenario and perioperative strategies for the management of coagulation anomalies in patients with liver dysfunction.

Exhaustive simulation approach for severe accident risk in nuclear power plants: OPR-1000 full-power internal events

The estimation of severe accident risks in nuclear power plants (NPPs) is significant to confirm the safety level of NPPs and to reinforce their vulnerable points. Severe accident risks can be quantified by Level 2 probabilistic safety assessment (PSA), which traditionally applies a grouping feature to handle the tremendous numbers of accident scenarios. Accordingly, risk information is likely to be lost in the process of grouping similar scenarios and in the treatment of many scenarios with one representative scenario. To obtain more comprehensive risk information including source term behaviors and plant responses during severe accidents, this study suggests an exhaustive simulation approach with new software that helps to automatically generate a large number of input data for an accident simulation code, and performs an application study using PSA models from OPR-1000 full-power internal events. Only a three-day run time was required to simulate all the severe accident scenarios, totaling 690 scenarios, using a commercial computer. The application study revealed that the conventional grouping approach can either underestimate or overestimate overall NPP risk depending on the selection of the representative scenario.

Machine learning-based categorization of source terms for risk assessment of nuclear power plants

In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.

A Student Grouping Method for Massive Online Collaborative Learning

As a brand-new learning strategy, cooperative learning can greatly improve the quality of classroom teaching and the teamwork and learning efficiency of students. The conventional student grouping models for cooperative learning take less consideration of the complementarity of students’ knowledge structures and learning interests, so they are not quite suitable for online teaching, which is provided for a huge number of students and has a large scale of learning resources and great freedom of choice. To this end, this paper studies the student grouping method for massive online collaborative learning. Firstly, the problem of identifying the student’s knowledge state was described and characterized, and a diagnosis model of students’ knowledge state simulating the cooperative learning process was constructed based on the gated recurrent neural network. The creation of an online collaborative learning scenario that considers both the knowledge state and interests of students was regarded as an NP-hard problem, and then, the enhanced particle swarm optimization algorithm was adopted to realize the grouping of students for massive online collaborative learning. The experimental results verified the effectiveness of the model constructed in this paper.

G2P-SLAM: Generalized RGB-D SLAM Framework for Mobile Robots in Low-Dynamic Environments

In this paper, we propose a generalized grouping and pruning method for RGB-D SLAM in low-dynamic environments. The conventional grouping and pruning methods successfully reject the effect of dynamic objects in pose graph optimization (PGO). However, these methods sometimes fail when high-dynamic objects are dominant in the images captured by RGB-D sensors. Furthermore, once it is determined whether the features from dynamic objects are included in some nodes, the corresponding nodes are entirely removed even though these nodes partially include true constraints, which leads to an inaccurate PGO. To tackle these problems, we propose a novel method with intra-grouping, inter-grouping, and selective pruning, called G2P-SLAM. Accordingly, our method successfully rejects false constraints from dynamic objects selectively, thus preserving true constraints from static objects as many as possible. As experimentally verified on both our own datasets and public datasets, our proposed method shows promising performance compared with the state-of-the-art methods. Furthermore, experimental results corroborate that our G2P-SLAM enables robust PGO in both dynamic and low-dynamic environments.

Refined HLA-DPB1 mismatch with molecular algorithms predicts outcomes in hematopoietic stem cell transplantation.

HLA-DPB1 mismatches between donor and recipient are commonly seen in allogeneic hematopoietic stem cell transplantation from an unrelated donor. HLA-DPB1 mismatch, conventionally determined by the similarity of the T-cell epitope (TCE), is associated with an increased risk of acute graft-versus-host disease (GVHD) and a decreased risk of disease relapse. We investigated the clinical impact of HLA-DPB1 molecular mismatch quantified by mismatched eplets (ME) and the Predicted Indirectly Recognizable HLA Epitopes Score (PS) in a cohort of 1,514 patients receiving hematopoietic stem cell transplants from unrelated donors matched at HLA-A, -B, -C, -DRB1/3/4/5, and - DQB1 loci. HLA-DPB1 alloimmunity in the graft-versus-host direction, determined by high graft-versus-host ME/PS, was associated with a reduced risk of relapse (hazard ratio [HR]=0.83, P=0.05 for ME) and increased risk of grade 2-4 acute GVHD (HR=1.44, P<0.001 for ME), whereas high host-versus-graft ME/PS was only associated with an increased risk of grade 2-4 acute GVHD (HR=1.26, P=0.004 for ME). Notably, in the permissive mismatch subgroup classified by TCE grouping, high host-versus-graft ME/PS was associated with an increased risk of relapse (HR=1.36, P=0.026 for ME) and grade 2-4 acute GVHD (HR=1.43, P=0.003 for PS-II). Decision curve analysis showed that graftversus- host ME outperformed other models and provided the best clinical net benefit for the modification of acute GVHD prophylaxis regimens in patients with a high risk of developing clinically significant acute GVHD. In conclusion, molecular assessment of HLA-DPB1 mismatch enables separate prediction of host-versus-graft or graft-versus-host alloresponse quantitatively and allows further refinement of HLA-DPB1 permissiveness as defined by conventional TCE grouping.

Optimized matching between students of the automation major and their specialized project design assignment

Specialized project design (SPD) is a compulsory course for most of the engineering students in China. A SPD course requires teamwork of students. It is therefore an important issue about how to group students in a more reasonable form to increase students’ interest in the course and improve their performance. However, at present, it remains a challenge to quantitatively assess the effectiveness of group teaching and optimize group teaching through state-of-the-art methods such as artificial intelligence. In this study, we propose a qualitative analysis method to study the group teaching of students through teacher’s subjective evaluation of different aspects of students’ abilities. In particular, the proposed method is a background-based grouping method which takes into account the students’ scores attained in prerequisite courses of a SPD course and realized by a genetic algorithm. Results of our experiment demonstrate that, compared with the conventional random grouping method, the proposed method improves the students’ average scores in all eight SPD topics by 2.5 in the grade 2016 and 1.875 in grade 2017, respectively (the total score is 100). Therefore, our findings strongly support that grouping students based on their strengths (for example, the courses they are good at) can enhance students’ interest in the SPD course and thereby improve students’ SPD performance.

Image Segmentation Using A Neoteric-Adaptive Fusion Of Fuzzy C-Means Clustering Model And Fuzzy Svm

Image Clustering processes have effectively remained as an alphanumeric method of image separation method in many arenas and applications. A clustering algorithm screens records that are keen on numerous groups in such a way that the resemblance inside a group is enhanced than between groups. On the other hand, those grouping procedures are merely relevant for explicit images like medicinal images, atomic pictures, etc. In this paper, we extant a novel grouping procedure grounded on A Neoteric-Adaptive Fusion of Fuzzy C-Means (NAF-FCM) for image separation which might be useful on broad images and exact images like medicinal and atomic images, seized using communal digital cameras and Charged-Couple Device cameras. The procedure involves the notions of fuzziness and belongingness to offer an improved and further adaptive grouping process as matched to numerous conventional grouping procedures. Both high quality and quantifiable investigates favor the projected grouping procedure in terms of furnishing an improved segmentation enactment for several numbers of segmented regions. This work incorporates image segmentation using a fusion of fuzzy c-means clustering model with fuzzy SVM classification to identify the changed areas using remote sensing images. The proposed algorithm is concentrated on fast and exact clustering. Grounded on the consequences assimilated, the projected system contributes an enhanced visual quality and its performance is compared with the conventional K-means clustering procedure. The result acquired from the proposed Neoteric Adaptive clustering process is far better than the conventional K-mean procedure.

Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer

Simple SummaryProstate cancer treatment decisions are based on clinical stage and histological diagnosis, including Gleason grading assessed by a pathologist, in biopsies. Prior to staging and grading, serum or blood prostate-specific antigen (PSA) levels are measured and often trigger diagnostic examinations. However, PSA is best suited as a marker of cancer relapse after initial treatment. In this review, we first narratively describe the evolution of histological grading, the current status of Gleason pattern-based diagnostics and glance into future methodology of risk assessment by histological examination. In the second part, we systematically review the biomarkers that have been shown, independent from clinical characteristics, to correlate with clinically relevant end-points, i.e., occurrence of metastases, disease-specific mortality and overall survival after initial treatment of localized prostate cancer.Gleason grading remains the strongest prognostic parameter in localized prostate adenocarcinoma. We have here outlined the evolution and contemporary practices in pathological evaluation of prostate tissue samples for Gleason score and Grade group. The state of more observer-independent grading methods with the aid of artificial intelligence is also reviewed. Additionally, we conducted a systematic review of biomarkers that hold promise in adding independent prognostic or predictive value on top of clinical parameters, Grade group and PSA. We especially focused on hard end points during the follow-up, i.e., occurrence of metastasis, disease-specific mortality and overall mortality. In peripheral blood, biopsy-detected prostate cancer or in surgical specimens, we can conclude that there are more than sixty biomarkers that have been shown to have independent prognostic significance when adjusted to conventional risk assessment or grouping. Our search brought up some known putative markers and panels, as expected. Also, the synthesis in the systematic review indicated markers that ought to be further studied as part of prospective trials and in well characterized patient cohorts in order to increase the resolution of the current clinico-pathological prognostic factors.

Physico-theoretical basis of the mesoscopic approach to the study of soil and climatic regulations

Aim of the study. The aim of the work was to review critically the practice of applying system approach in soil research and identify existing problems; to consider the cognitive potential of micro- and macroscopic approaches in computational simulation of soil-bioclimatic regularities. Methodology. Mathematical simulation. Results of the study. A new mesoscopic approach to the study of the structure of soil-bioclimatic areas is proposed. It is based on two principles referred to as the principle of invariance and the principle of optimality. Application of these principles makes it possible to express the regularities in soil-climate relationship in the form of a single universal quantitative dependence. The study showed that a mathematical model built on the basis of these principles resulted in the emergence of new generalized concepts, logically combining the previously considered to be unrelated concepts of hydro- and thermal series introduced by V.R. Volobuev, the Vysotsky - Ivanov moisture coefficient and the radiation dryness index by M. Budyko. In addition, the simulation showed that the concepts elucidating the indicator of the radiation energy utilization by biogeocenosis were represented by the interrelated coefficients of the heat and precipitation utilization in soils. The coefficients were also shown to have specific values ​​for different soil-bioclimatic areas. On the basis of the new indicators, soils were grouped in such a way as to produce patterns essentially different in many details from the conventional grouping. Conclusions. The new concepts represent a unified integral system, and the approach as a whole can be considered as an alternative physico-theoretical way to describe natural laws and regularities by using extreme (variational) methodology.

Performance Improvement of Clustering Affinity Propagation Method using Principal Component Analysis

Affinity Propagation Method it is necessary to modify the algorithm by using Principal Component Analysis (PCA). PCA method is used to reduce the attributes or characteristics that are less influential on the data so that the most influential attributes are obtained to then be carried out the clustering process with Affinity Propagation. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when there are 8 optimal cluster clusters. While the performance of conventional clustering models produces an optimal number of clusters from 14 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Air Quality dataset because it produces an optimal number of clusters and iterations of 8 clusters. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when the optimal number of clusters is 5 clusters. While the performance of conventional clustering models produces a suboptimal number of 10 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Water Quality Status dataset because it produces an optimal number of clusters and 5 cluster repetitions.

Molecular characterization and DNA methylation profile of Libyodrilus violaceous from oil polluted soil.

Studies on earthworms using molecular markers are rare in Africa except a handful from South Africa. Reports on Libyodrilus violaceous, an earthworm found in West Africa are available including their metal tolerance and bioaccumulation capacity but their molecular characterization and ecotoxicology studies are scarce. In this study, triplicate L. violaceous specimens were collected from four locations within a petroleum polluted site and one in a control site, ≃1Km away from point of spill. DNA was extracted and 18S rRNA and 16S rRNA genes were amplified and sequenced. DNA methylation of their 18S rRNA gene was determined using Methylation specific PCR (MSP) method. Phylogenetic trees generated for 18S rRNA and 16S rRNA genes grouped L. violaceous within the Eudrilidae family concurrent with its conventional grouping and MSP results indicate no methylation in L. violaceous population from this site.

Fair and efficient resource allocation in IEEE 802.11ah WLAN with heterogeneous data rates

For dense wireless LANs (WLANs), IEEE 802.11ah has specified restricted access window (RAW) as the channel access scheme. Here the competing stations (STAs) are divided into groups and STAs belonging to each group attempt to access the channel during their designated RAW slot by invoking the distributed coordination function (DCF) protocol. This paper describes an analytical model to evaluate the throughput performance of IEEE 802.11ah WLAN, when STAs use distinct data rates and the RAW mechanism is implemented for medium sharing. When STAs are grouped randomly (i.e., without considering their data rates), a group will contain STAs operating at distinct data rates. Since all these STAs simultaneously contend for channel access based on DCF protocol, the throughput of high data rate STAs are down-equalized to that of lower data rate STAs; thus the aggregate network throughput is degraded significantly. To resolve the resulting performance anomaly problem, we consider data rate based grouping where STAs operating at the same data rate are grouped together. We describe an algorithm for implementing data rate based grouping at the access point (AP). Further, we describe an analytical procedure to find the network throughput under data rate based grouping. Through numerical and experimental investigations, we establish that data rate based grouping can significantly improve the aggregate network throughput performance, as compared to the conventional random grouping strategy. Further, we use Jain’s fairness index (JFI) to establish that data rate based grouping can also provide fair resource allocation among the STAs operating at distinct data rates, by ensuring that all the competing STAs in the network achieve throughput proportional to their data rates.

Are weak blood groups important to look for in kidney transplantation? A case report on interchanging blood groups

The principles of ABO compatibility are fundamental to clinical solid organ transplantation, and conventional blood grouping techniques may mislabel patients with rare blood groups. We present a case of a kidney donor and her recipient who were mislabeled by conventional blood grouping methodology due to the presence of weak blood groups in both individuals, which were identified on detailed analysis, thus avoiding the need to undergo unnecessary desensitization protocols. The recipient underwent a successful transplant with the same donor and continues to do well 6 months after the transplant.

Improvement in Computation Time of the Finite Multipole Method by Using K-Means Clustering

K- means algorithm is one of the most widely used clustering algorithms in unsupervised machine learning. In the K- means clustering, input data are classified into the corresponding cluster based on the nearest mean criteria. We utilize the K- means clustering to create groups of basis functions in the finite multipole method. By replacing the conventional cube grouping with the K- means clustering, convergence rate in an iterative solver is improved. The computation time is thus alleviated without loss of the calculation accuracy. The performance of the proposed scheme is demonstrated by using both canonical and realistic computer-aided design models.