Cluster Centers Research Articles

Center-enhanced video captioning model with multimodal semantic alignment

Video captioning aims at automatically generating descriptive sentences based on the given video, establishing an association between the visual contents and textual languages, has attracted great attention and plays a significant role in many practical applications. Previous researches focus more on the aspect of caption generation, ignoring the alignment of multimodal feature and just simply concatenating them. Besides, video feature extraction is usually done in an off-line manner, which leads to the fact that the extracted feature may not adapted to the subsequent caption generation task. To improve the applicability of extracted features for downstream caption generation and to address the issue of multimodal semantic alignment fusion, we propose an end-to-end center-enhanced video captioning model with multimodal semantic alignment, which integrates feature extraction and caption generation task into a unified framework. In order to enhance the completeness of semantic features, we design a center enhancement strategy where the visual–textual deep joint semantic feature can be captured via incremental clustering, then the cluster centers can serve as the guidance for better caption generation. Moreover, we propose to promote the visual–textual multimodal alignment fusion by learning the visual and textual representation in a shared latent semantic space, so as to alleviate the multimodal misalignment problem. Experimental results on two popular datasets MSVD and MSR-VTT demonstrate that the proposed model could outperform the state-of-the-art methods, obtaining higher-quality caption results.

A mean shift algorithm incorporating reachable distance for spatial clustering

Spatial clustering is a widely used technique in spatial analysis that groups similar objects together based on their proximity in space. However, traditional clustering algorithms often fail to ensure the accessibility of cluster centers, which limits their validity in practical applications such as facility location problems. To address this issue, this article introduces a novel Mean Shift algorithm that incorporates reachable distance and an iterative mechanism to accurately locate cluster centers. The proposed algorithm initially labels clustering elements with road network coordinates to facilitate the calculation of reachable distance and the cluster center iterative mechanism. Subsequently, the mean shift vector function is modified to employ reachable distance as the measure of geographic reachable similarity. Unlike existing algorithms, our approach allows for cluster centers to be positioned independently of the clustering elements, guaranteeing geographical accessibility. Through simulation experiments, we demonstrate that our proposed algorithm not only outperforms existing methods in terms of solution quality, but also effectively addresses the limitations of disregarding geographical obstacles and unreachable cluster centers.

A Clustering Pruning Method Based on Multidimensional Channel Information

Pruning convolutional neural networks offers a promising solution to mitigate the computational complexity challenges encountered during application deployment. However, prevalent pruning techniques primarily concentrate on model parameters or feature mapping analysis to devise static pruning strategies, often overlooking the underlying feature extraction capacity of convolutional kernels. To address this, the study first quantitatively expresses the feature extraction capability of convolutional channels from three aspects: global features, distribution metrics, and directional metrics. It explores the multi-dimensional information of the channels, calculates the overall expectation, variance, and cosine distance from the unit vector as the quantitative results of the channels. Subsequently, a clustering algorithm is employed to categorize the multidimensional information. This approach ensures that convolutional channels grouped within each cluster possess similar feature extraction capabilities. An enhanced differential evolutionary algorithm is utilized to optimize the number of clustering centers across all convolutional layers, ensuring optimal grouping. The final step involves achieving channel sparsification through the calculation of crowding distances for each sample within its designated cluster. This preserves a diverse subset of channels that are critical for maintaining model accuracy. Extensive empirical evaluations conducted on three benchmark image classification datasets demonstrate the efficacy of this method. For instance, on the ImageNet dataset, the ResNet-50 model experiences a substantial reduction in FLOPs by 58.43% while incurring a minimal decrease in TOP-1 accuracy of only 1.15%.

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.

Trends and Collaborations in Livestock Extension Research: A Bibliometric Analysis (2015-2024)

This study employs bibliometric analysis to investigate trends in livestock extension research from January 2015 to June 2024, utilizing data sourced from the dimension.ai database. The analysis encompasses a comprehensive review of publication trends, research categories, influential journals, collaborative networks among authors, and the impact of seminal studies. The findings reveal a notable increase of 51.55 per cent in scholarly interest and publications over the years, indicating growing attention towards enhancing livestock productivity, sustainability, and socio-economic outcomes. Key research domains such as agricultural, veterinary, and food sciences emerge as predominant areas of focus, with significant contributions observed in interdisciplinary fields like environmental sciences and human geography. Network analyses using tools like VOSviewer highlight robust collaborative relationships among researchers, identifying central figures and clusters that drive collaborative research efforts in livestock extension. The study also identified “Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan” published in 2016, with nearly 294 citations as top-cited article and other affiliations, emphasizing their pivotal roles in advancing knowledge and practices within the field. Overall, this bibliometric analysis provides valuable insights into the evolving landscape of livestock extension research, demonstrating how such analytical approaches contribute to understanding research trends, fostering interdisciplinary collaborations, and guiding future research directions in agricultural sciences.

Sectoral electricity micro-spatial load forecasting based on partitional clustering technique

<p>Load demand forecasting is crucial in energy supply planning due to economic progress and territorial expansion, where land utilization transforms dynamically. An accurate sectoral load prediction can preclude the loss of beneficial opportunities arising from excessive load demand or excessive investment at a low-growth juncture. However, the particular area in this sectoral approach is still relatively large, rendering it incapable of precisely projecting load at minor points (micro-spatial). This study has proposed a micro-spatial load prediction strategy that categorizes identified areas into smaller grids or districts. This procedure includes clustering similar sites together for improved accuracy. K-Means is one of the partitional clustering approaches, a clustering algorithm utilizing object-based centroid-based partitioning approaches. The algorithm determines a cluster's centroid or centre as the average point for the cluster. This technique is advantageous as it can process extensive data efficiently and is appropriate for circular data. This technique can divide the data into multiple partitions, ensuring that each object belongs to precisely one cluster. Subsequently, mathematical modelling is used to predict the load of each cluster, which can then be utilized to more accurately evaluate the positions and sizes of prospective substations, transmission, and distribution facilities.</p>

The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR.

This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted γ curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters.

An investigation of anisotropy in the bubbly turbulent flow via direct numerical simulations

We investigated the effects of bubble count, flow direction, and Eötvös number on deformable bubbles in turbulent channel flow. For a given shear Reynolds number Re = 180 and fixed bubble volume fractions (1.263% and 2.525%), we conducted a series of direct numerical simulations using a coupled level-set and volume-of-fluid solver to evaluate their impact on bubble volume fraction distribution, velocity fields, and turbulence characteristics. Each aspect was studied based on the microscopic equations of two-phase flow, and the accuracy of the modeling terms used in current Reynolds-averaged Navier–Stokes equation (RANS) models was assessed. The influence on the anisotropic state was analyzed using the Lumley triangle, and the anisotropy of Reynolds stresses was captured through the exact balance equations. The results indicate that in upward flow, bubbles tend to accumulate near the wall, with smaller Eötvös numbers leading to closer proximity to the wall and greater attenuation of the liquid-phase velocity. This distribution enhances energy dissipation and turbulence isotropy. In downward flow, bubbles cluster in the channel center, generating additional pseudo-turbulence and attenuating energy in the buffer layer. Moreover, the interfacial transfer of turbulent energy, as currently modeled in RANS, is found to be inadequate for upward flows.

A Novel Classification Model Based on Hybrid K-Means and Neural Network for Classification Problems

We propose a new classification model—a new classification model for clustering overlapping problems based on K-Means and neural networks. K-means clustering algorithm belongs to unsupervised learning. It is a classic algorithm for solving clustering problems. Since this algorithm calculates its categories based on distance, the results tend to converge to the local optimal solution and have poor boundary clustering properties. The K-Means classification algorithm defines clusters by the distance between the cluster center value and the target object, and the optimal result is obtained through continuous iteration. Therefore, clustering results are overlapped, and there are often outliers that do not belong to the current cluster, resulting in unsatisfactory clustering results. Our model offers a new method to segment non-ideal data in overlapping regions. Since clustering algorithms cannot effectively identify and classify this part of the data, we split this part of the data and train it using a neural network. The results are then integrated into the clustered data. In the experiment, the k-fold cross-validation method ensures the model stability of the results. We used the accuracy to evaluate the quality of the model, and we used standard deviation and mean deviation to detect clustering results. Five sets of experimental data from the cross-experiment show that compared with the K-Means classification model, the accuracy of our model is effectively improved. Doi: 10.28991/HIJ-2024-05-03-012 Full Text: PDF

Optical polarimetry study of the Lambda-Orionis star-forming region

We present an optical polarimetry study of the nearby star-forming region Lambda-Orionis to map the plane-of-the-sky magnetic field geometry to understand the magnetized evolution of the HII region and the associated small molecular clouds. We made multiwavelength polarization observations of 34 bright stars distributed across the region. We also present the R-band polarization measurements that focused on the small molecular clouds, bright-rimmed clouds (BRC), BRC 17, and BRC 18, which are located at the periphery of the HII region. The magnetic field lines exhibit a large-scale ordered orientation consistent with the Planck submillimeter polarization measurements. The magnetic field lines in the two BRCs are found to be roughly in north-south directions. However, a larger dispersion is noted in the orientation for BRC 17 compared to BRC 18. Using a structure-function analysis, we estimate the strength of the plane-of-the-sky component of the magnetic field as ∼28 μG for BRC 17 and ∼40 μG for BRC 18. The average dust grain size and the mean value of the total-to-selective extinction ratio (RV) in the HII region are found to be ∼0.51 ± 0.05 μm and ∼2.9 ± 0.3, respectively. The distance of the whole HII region is estimated as ∼392 ± 8 pc by combining astrometry information from Global Astrometric Interferometer for Astrophysics (Gaia) early data release 3 (EDR3) for young stellar objects associated with BRCs and confirmed members of the central cluster Collinder 69.

A fuzzy K-Means algorithm based on Fisher distribution for the identification of rock discontinuity sets

Rock discontinuities significantly impact the mechanical and hydraulic behavior of rock masses. A crucial aspect of rock engineering involves classifying discontinuities with similar orientations into groups. For this purpose, clustering algorithms, such as K-Means and Fuzzy K-Means (FKM), have been employed. However, the outcomes of these algorithms are influenced by the selection of initial cluster centers. This paper proposes an improved FKM algorithm to automatically identify rock discontinuity sets based on the Fisher distribution (FFKM). The algorithm uses the Fisher distribution to generate and select appropriate initial cluster centers. The performance of FFKM was initially validated using a published data set, and its results were compared with other clustering methods commonly used for grouping discontinuities. Results demonstrated the superior performance of FFKM over the FKM algorithm, comparable to other methods. Subsequently, the proposed algorithm was employed to analyze a fracture data set sampled at an open-pit iron mine. The FFKM facilitated identifying the correct number of sets and produced results consistent with the fracture sets observed in the field. Finally, the algorithm was verified using an artificial discontinuity data set, and the results demonstrated that the method correctly identified the number of sets and provided discontinuity sets similar to the original data set. The FFKM algorithm offers significant advantages: it maintains the essential characteristics of the FKM algorithm, effectively addresses the challenge of selecting suitable initial cluster centers, requires only the expected number of discontinuity sets as an input parameter, processes data within an acceptable computation time, serves as a tool for defining the number of discontinuity sets, and mitigates the drawbacks of the stereographic projection method.

Behavioral analysis of electricity consumption characteristics for customer groups using the k-means algorithm

In the fierce competition of the electricity market, how to consolidate and develop customers is particularly important. Aiming to analyze the electricity consumption characteristics of customer groups, this paper used a k-means algorithm and optimized it. The number of clusters was determined by the Davies-Bouldin index (DBI). An improved Harris Hawks optimization (IHHO) algorithm was designed to realize the initial cluster center selection. Based on data such as electricity purchase and average electricity price, electricity customer groups were clustered using the IHHO-k-means algorithm. The IHHO-k-means algorithm achieved the best clustering effect on Iris, Wine, and Glass datasets compared with the traditional k-means and PSO-k-means algorithms. Taking Iris as an example, the optimal value of the IHHO-k-means algorithm was 96.538, with an accuracy rate of 0.932, precision and recall rates of 0.941 and 0.793, respectively, an F-measure of 0.861, and an area under the curve (AUC) value of 0.851. In the customer dataset, the number of clusters determined by DBI was 4. The power customers were divided into four groups with different characteristics of electricity consumption, and their electricity consumption behaviors were analyzed. The results prove the reliability of the IHHO-k-means algorithm in analyzing electricity consumption characteristics of customer groups, and it can be applied in practice.

ECG arrhythmia classification based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability

Electrocardiograph (ECG) is one of the most critical physiological signals used for arrhythmia diagnosis. In recent years, ECG arrhythmia classification devices consisting of multi-module sensors, clustering algorithms and neural networks play an important role in monitoring and diagnosing cardiovascular diseases. However, the commonly used ECG arrhythmia classification methods are still facing some problems such as the complex model structure and long running time. To address the above problems, this paper proposes an ECG arrhythmia classification method based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability (SFP-FACC), which uses LSTM to fit the cluster centers and avoids the time consumption of updating the cluster centers during the classification process. The spatiotemporal feature perception ability of this model with the dynamic time warping (DTW) algorithm is improved. The classification is achieved by applying the combination of Euclidean distance and DTW. The convergence speed of the model is improved by using dynamic pheromone volatility coefficient; and finally the optimal solution of the model is determined by using radix sort. Based on the MIT-BIH arrhythmia dataset, the overall accuracy of the proposed classification method in this paper achieves 99.04 %, and even the accuracy of certain types of classification achieves 100 %, and the running time is about 3.5 times faster than that of the basic models. The experiments show that the method proposed in this paper has certain advantages.

Influence of Al-clustering in the catalytic activity of layered double hydroxides for the Baeyer-Villiger oxidation

The utilization of efficient and environmentally friendly heterogeneous catalysts in the Baeyer-Villiger (BV) reaction gained significant importance in the field of green chemistry. In this study, the catalytic properties of magnesium-aluminium hydrotalcite in the BV oxidation of cyclic ketones using H2O2 were investigated. The research focused on the influence of two different synthetic routes on the clustering of aluminium centers and its impact on catalytic activity in the Baeyer-Villiger reaction. Surprisingly, the catalyst with a high degree of aluminium clustering exhibited superior performance compared to the ordered aluminium solid. Moreover, the most active catalyst displayed sustained activity even after three successive reaction cycles. This study presented the first report on the influence of trivalent metals in the BV reaction, thus opening up a new avenue for further exploration of such catalysts in the context of Baeyer-Villiger oxidation.

Fault diagnosis of satellite power system based on unsupervised knowledge acquisition and decision-making

Fault diagnosis (FD) is an important foundation for the maintenance of complex aerospace systems, such as satellite power systems, in which the attribute reduction has essential effect to eliminating data redundancy and improving diagnostic results. However, due to the difficulty and high cost of obtaining labels in some situations, especially early failures, FD based on unsupervised methods is of great significance but less commonly-studied. Moreover, with respect to FD preprocessing, unsupervised attribute reduction (UAR) usually applying clustering methods suffers from the need for cluster number, randomness, inability to handle non-spherical clusters, etc. Therefore, this paper proposes an unsupervised FD strategy including a knowledge acquisition method to mine the rules from the unlabeled data, a decision-making method to process the acquired knowledge, and a diagnosis decision for the fault identification. As for the preprocessing part, this paper proposes a wrapper UAR method (named DPC-UAR) based on the density peak clustering (DPC) and heuristic method, which can automatically identify the cluster centers and deal with the nonspherical data. Finally, experiments of attribute reduction performance on UCI data show that compared with other UAR methods, DPC-UAR has the greatest effect to improve performance of unsupervised learning algorithms, and plays a relatively good role in the supervised algorithm. Experiments on satellite power system fault diagnosis illustrated that the proposed FD strategy based on DPC-UAR has high accuracy, a high fault detection rate, and a low false alarm rate.

EMS: A Large-Scale Eye Movement Dataset, Benchmark, and New Model for Schizophrenia Recognition.

Schizophrenia (SZ) is a common and disabling mental illness, and most patients encounter cognitive deficits. The eye-tracking technology has been increasingly used to characterize cognitive deficits for its reasonable time and economic costs. However, there is no large-scale and publicly available eye movement dataset and benchmark for SZ recognition. To address these issues, we release a large-scale Eye Movement dataset for SZ recognition (EMS), which consists of eye movement data from 104 schizophrenics and 104 healthy controls (HCs) based on the free-viewing paradigm with 100 stimuli. We also conduct the first comprehensive benchmark, which has been absent for a long time in this field, to compare the related 13 psychosis recognition methods using six metrics. Besides, we propose a novel mean-shift-based network (MSNet) for eye movement-based SZ recognition, which elaborately combines the mean shift algorithm with convolution to extract the cluster center as the subject feature. In MSNet, first, a stimulus feature branch (SFB) is adopted to enhance each stimulus feature with similar information from all stimulus features, and then, the cluster center branch (CCB) is utilized to generate the cluster center as subject feature and update it by the mean shift vector. The performance of our MSNet is superior to prior contenders, thus, it can act as a powerful baseline to advance subsequent study. To pave the road in this research field, the EMS dataset, the benchmark results, and the code of MSNet are publicly available at https://github.com/YingjieSong1/EMS.

Cold Filaments Formed in Hot Wake Flows Uplifted by Active Galactic Nucleus Bubbles in Galaxy Clusters

Multiwavelength observations indicate that the intracluster medium in some galaxy clusters contains cold filaments, while their formation mechanism remains debated. Using hydrodynamic simulations, we show that cold filaments could naturally condense out of the hot gaseous wake flows uplifted by jet-inflated active galactic nucleus (AGN) bubbles. Consistent with observations, the simulated filaments extend to tens of kiloparsecs from the cluster center, with a representative mass of 108–109 M ⊙ for a typical AGN outburst energy of 1060 erg. They show smooth velocity gradients, stretching typically from inner inflows to outer outflows with velocity dispersions of several hundred kilometers per second. The properties of cold filaments are affected substantially by the jet properties. Compared to kinetic-energy-dominated jets, it is easier for thermal-energy-dominated jets to produce long cold filaments with large masses, as observed. AGN jets with an early turn-on time, a low jet base, or a very high power tend to overheat the cluster center and produce short cold filaments that take a relatively long time to condense out.

Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

Data-driven Bayesian Gaussian mixture optimized anchor box model for accurate and efficient detection of green citrus

The demand for harvesting green citrus has increased due to the rapid growth of the green citrus industry. However, real-time detection of green citrus is challenging because of its high visual similarity to the background. To address this issue, a new model called EDGC-YOLO (Efficient Detection of Green Citrus based on the YOLO) has been developed, focusing on detecting green citrus effectively and accurately. Specifically, the proposed model uses a data-driven approach that employs the highest confidence level from the aspect ratio of unobstructed citrus fruits to infer anchor boxes of obscured citrus. This method ensures a high degree of coincidence with bounding boxes based on empirical annotation. Furthermore, analyzing the distribution range of various citrus annotation categories within the dataset and the incorporating the highest confidence level of the normal distribution regarding aspect ratios, the initial cluster centers of the BGMM (Bayesian Gaussian Mixture Model) are dynamically adjusted. This strategy enables the anchor boxes to be inferred from the best suited for detecting green citrus. These improved anchor boxes provide a more accurate and effective solution for centroid positioning of obscured green citrus, significantly enhancing the entire network model’s detection accuracy and lightweight efficiency. To further improve detection accuracy and reduce model size, this study integrated the Refined-EfficientNetV2 network as the backbone, enhanced with a Convolutional Block Attention Module (CBAM) for better feature extraction. This integration allows the model to effectively capture relevant channel information and spatial features, increasing the feature extraction capability for green citrus images. Experimental results demonstrate the model’s superior performance: parameters reduced to 4.52 million, computational demand to 7.8 GFLOPs (64.4 % and 49.4 % of the original model, respectively), and model size decreased to 9.4 MB (65.3 % of the original). Additionally, the optimized model improved Precision (P), Recall (R), and mean Average Precision (mAP) by 0.5 %, 1.6 %, and 3.0 %, respectively, compared to the original model. The proposed model achieves higher detection accuracy with a smaller model size, providing theoretical support for the harvesting decision-making of green citrus harvesting robots.

K-Means text clustering method based on Decision Grey Wolf Optimization

Aiming at the problem that the traditional algorithm is easy to fall into local optimum in the process of text clustering, which leads to inaccurate text clustering results, a text clustering method based on decision grey wolf optimization K-Means is proposed to cluster the text data set and the standard UCI data set respectively. Afterword segmentation, stop words removal, feature extraction, and text vectorization of text data, the powerful optimization ability of the Decision Gray Wolf Optimization (DGWO) algorithm is used for global optimization, and the clustering center in K-Means algorithm is replaced by the location of wolves. The position of the wolf group is updated by iterative optimization to obtain the optimal clustering center, to perform text clustering. The experimental results show that compared with the traditional method, the precision, recall, and F-Measure of the text data clustering are improved by 49.22%, 51.15%, and 48.98% respectively. The precision, recall, and F-Measure of UCI data clustering are increased by 23.92%, 25.40%, and 24.70% respectively, and the text clustering results are more reliable.