Initial Cluster Research Articles

Intelligent scanning for optimal rock discontinuity sets considering multiple parameters based on manifold learning combined with UAV photogrammetry

The Sichuan-Tibet railway, which spans many alpine canyon regions, is being built in southwestern China. Investigating the characteristics of rock discontinuity sets is the basis for identifying dangerous rock masses above the tunnel portals. The traditional methods of identifying discontinuity sets usually consider orientations and ignore other parameters, which results in incorrect guidance for rock engineering. To this end, the affinity propagation (AP) algorithm based on modified isometric mapping (Isomap) is proposed for partitioning discontinuity sets based on orientation, trace length, and aperture. The new unsupervised algorithm (ISOAP) uses manifold learning to complete the transformation process for orientations from spherical vectors to scalars and avoids selecting the initial clustering center to achieve global optimization. The Silhouette Index is used to intelligently scan the optimal clustering results. The proposed algorithm is tested on a complex artificial data set and on Shanley and Mahtab's data set. Since accurately obtaining discontinuity information is impossible by traditional means (i.e., using geological compasses and measurement tapes) due to the existence of a mass of high and steep slopes, the ISOAP algorithm is combined with semiautomatic technology based on unmanned aerial vehicle (UAV) photogrammetry and applied to a rock slope located along the railway. The introduction of manifold learning is beneficial for quickly applying abundant unmodified clustering algorithms to rock engineering and searching the optimal algorithm suitable for analyzing the structural characteristics of a specific fractured rock mass. The proposed method can simplify rock engineering analyses and provide more reasonable results.

Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM.

Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts.

Robust intuitionistic fuzzy clustering with bias field estimation for noisy image segmentation

The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms.

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures.

Research on Industrial Aggregation and Spatial Development: Jingzhou High-tech Zone as Example

Jingzhou High-tech Zone is an industrial industry agglomeration in Jingzhou. The overall level of high-tech industry development in Jingzhou is not high. After experiencing high growth in recent years, it gradually enters a difficult period of industrial development due to the macroeconomic environment and lacks a large contiguous land, adjustment of new city planning, and generalization of policies, compared to similar development zones. We selected 24 representative indicators for the industrial and spatial development of the Jingzhou industry and High-tech Zone to construct an evaluation system for industrial development in Jingzhou. SPSS principal component analysis method was used for validating the system. We analyzed several factors affecting the industrial clustering of enterprises in the Jingzhou high-tech zone by referring to the existing analysis method of industrial spatial aggregation to construct a calculation model for the industrial spatial aggregation of the Jingzhou high-tech zone. The industrial spatial aggregation of 49 enterprises in the Jingzhou high-tech zone was calculated, too. The results of principal component analysis and the calculation results of the industrial aggregation degree of 49 enterprises showed that the industrial development of Jingzhou was positive in time and space, and six items, such as industrial turnover of movable assets, industrial debt rate, industrial loss rate, industrial secondary industry fixed investment, industrial employment population, and sewage treatment rate affected the zone’s industrial development.

Binary black hole mergers from young massive clusters in the pair-instability supernova mass gap

Context. The recent discovery of the binary black hole (BBH) merger event GW190521, between two black holes (BHs) of ≈100 Msamp, in addition to other massive BBH merger events involving BHs within the pair-instability supernova (PSN) mass gap have sparked widespread debate on the origin of such extreme gravitational-wave (GW) events. GW190521 simultaneously triggers two critical questions: how BHs can appear within the ‘forbidden’ PSN gap and, if they do, how they get to participate in general-relativistic (GR) mergers. Aims. In this study, I investigate whether dynamical interactions in young massive clusters (YMCs) serve as a viable scenario for assembling PSN-gap BBH mergers. Methods. To that end, I explore a grid of 40 new evolutionary models of a representative YMC of initial mass and size Mcl = 7.5 × 104 Msamp (N ≈ 1.28 × 105) and rh = 2 pc, respectively. The model grid ranges over metallicity 0.0002 ≤ Z ≤ 0.02 and comprises initial cluster configurations of King central concentration parameters W0 = 7 and 9. In each model, all BH progenitor stars are initially in primordial binaries following observationally motivated distributions. All cluster models are evolved with the direct, relativistic N-body code NBODY7, incorporating up-to-date remnant formation, BH natal spin, and GR merger recoil schemes. Results. Binary black hole mergers from these model cluster computations agree well with the masses and effective spin parameters, χeff, of the events from the latest gravitational-wave transient catalogue (GWTC). In particular, GW190521-like, that is to say ≈200 Msamp, low χeff events are produced via a dynamical merger among BHs derived from star-star merger products. GW190403_051519-like, that is PSN-gap, highly asymmetric, high χeff events result from mergers involving BHs that are spun up via matter accretion or a binary interaction. The resulting present-day, differential intrinsic merger rate density, within the PSN gap, accommodates that from GWTC well. Conclusions. This study demonstrates that, subject to model uncertainties, the tandem of massive binary evolution and dynamical interactions in ≲100 Myr-old, low metallicity YMCs in the Universe can plausibly produce GR mergers involving PSN-gap BHs and in rates consistent with that from up-to-date GW observations. Such clusters can produce extreme events similar to GW190521 and GW190403_051519. The upper limit of the models’ GW190521-type event rate is within the corresponding LIGO-Virgo-KAGRA (LVK)-estimated rate limits, although the typical model rate lies below LVK’s lower limit. The present YMC models yield a merger rate density of 0−3.8 × 10−2 yr−1 Gpc−3 for GW190521-type events. They produce GW190403_051519-like events at a rate within 0−1.6 × 10−1 yr−1 Gpc−3 and their total BBH-merger yield within the PSN gap is 0−8.4 × 10−1 yr−1 Gpc−3.

Interdentals to the rescue: a German four-year-old with protracted phonological development

ABSTRACT This paper examines the phonology of a German four-year-old with asynchronous phonological development: age-level word structure and vowels, but a restricted consonantal inventory. Both expected and unexpected developmental patterns were evident. While the absence of dorsal non-continuants and most coronal and labiodental fricatives and affricates is not unexpected in four-year-olds with PPD, less expected was his mastery of more marked /l/ and /ʁ/ and the pervasive use of ungrooved interdental fricatives (or affricates) as substitutions. Ungrooved interdentals often replace grooved fricatives and affricates, but in his system, they also replaced stops in more marked prosodic positions (unstressed initial syllables and clusters) and labiodental /f/. His case profile demonstrates the autonomy of phonological tiers (levels) but also the restrictive interactions that occur when one tier dominates, in his case, word structure. A proposed treatment plan targets new feature combinations and consonant sequences in order to address the asynchrony.

FC-Kmeans: Fixed-centered K-means algorithm

Clustering is one of the data mining methods that partition large-sized data into subgroups according to their similarities. K-means clustering algorithm works well in spherical or convex data distribution of large-sized data sets. Most of the algorithms based on K-means have generally been interested in an initial cluster centers selection or cluster distribution. However, these algorithms may not meet satisfy some requirements in practice. This paper presents the FC-Kmeans algorithm, which enables clustering by fixing some cluster centers considering real conditions. Thus, while some of the cluster centers are fixed, it is tried to obtain the most appropriate cluster centers for the others and the best distribution of the data to the clusters. The K-means clustering algorithm is compared with two different fixed-centered clustering algorithms which are FC-Kmeans and FC-Kmeans 2. The experimental results show that although the FC-Kmeans algorithm has more limitations than K-means, it converges the performance of K-means algorithm according to some performance indicators such as SSE, DB Index and Silhouette Index.

Information Architecture: Using Best Merge Method, Category Validity, and Multidimensional Scaling for Open Card Sort Data Analysis

Open card sorting is a widely used method in HCI for the design of user-centered Information Architectures (IAs). This article proposes a new algorithm that combines the best merge method (BMM), category validity technique (CVT), and multidimensional scaling (MDS) to explore, analyze and visualize open card sort data. A study involving 20 participants and 41 cards explored the IA redesign of a university’s website. The collected data were analyzed using two popular methods employed in the quantitative analysis of open card sort data (i.e., hierarchical clustering, K-means) and the proposed algorithm. It was found that the latter provides increased IA insights compared to the existing methods. Specifically, the proposed algorithm can expose hidden patterns and relationships amongst cards and identify complexities. We also found that the proposed algorithm produces better initial clusters, which have a direct effect on the final clustering quality.

Spatiotemporal evolution of seismicity during the cyclic operation of the Hutubi underground gas storage, Xinjiang, China

Underground gas storages (UGSs) are important large-scale industrial facilities used to bridge the gap between natural gas consumption and supply. The cyclic operation of the UGS may alter the subsurface stresses and local seismicity. We examined seismicity around the Hutubi UGS from 2011 to 2019 using the matched filter technique (MFT) and double-difference location methods. More than 1300 earthquakes were detected with seismicity around the UGS showing a remarkable increase since the start of its operation and showing a clear correlation to seasonal gas production. About 684 detected earthquakes were located, most of them occurred within 6 km of the reservoir. The events can be grouped into two clusters. Both clusters initiated around the gas pressure boundary. The first cluster extinct after the first injection period. While the second cluster diffused upward along a pre-existing fault. We speculate that strain localization caused by non-uniform gas injection contributes to the initiation of seismicity clusters around the UGS, and the trapped crude oil/gas played an important role in the migration of the second surge. The revealed seismicity pattern contributes to a better understanding of the mechanism of induced seismic events and emphasizes the importance of seismic monitoring in the UGS region.

Collaborative annealing power [formula omitted]-means++ clustering

Clustering is the most fundamental technique for data processing. This paper presents a collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ and power k-means algorithms in a collaborative neurodynamic optimization framework. The proposed algorithm starts with k-means++ to select initial cluster centers, then leverages the power k-means to find multiple sets of centers as alternatives and a particle swarm optimization rule to reinitialize the centers in the subsequential iterations for improving clustering performance. Experimental results on twelve benchmark datasets are elaborated to demonstrate the superior performance of the proposed algorithm to seven mainstream clustering algorithms in terms of 21 internal and external indices.

Impact of knowledge absorptive capacity on innovative performance in SMEs: mediating effect of cluster environment

PurposeThis study builds on knowledge-based view (KBV) research and the natural resource-based view of the firm (NRBV) to examine the effect of knowledge absorptive capacity on innovative performance. It also investigates the mediating role of three dimensions of the cluster environment: degree of network, institutional environment and access to factors of production.Design/methodology/approachBy means of partial least squares-based structural equation modelling method, the study validated and analysed the responses of 503 owners and managers of small and medium-sized enterprises (SMEs) in the wood industrial cluster of Cote d'Ivoire, a developing economy.FindingsThe analysis and results reveal that knowledge absorptive capacity strongly predicts cluster environment and innovation performance. Moreover, the cluster environment emerged as a powerful determinant of innovation performance and a mediator of the effect of knowledge absorptive capacity on innovation performance. Unequivocally, institutional support has no significant impact on knowledge absorptive capacity (KAC) and innovative performance mediation relationship and is not a key determinant of innovative performance.Originality/valueThis study offers a key departure from past studies by linking knowledge absorptive capacity to innovative performance and the cluster environment in SMEs. It also extends knowledge concerning the role of KBV, NRBV and relational theory in innovation performance.

Clusteromics IV: The Role of Nitric Acid in Atmospheric Cluster Formation.

Nitric acid (NA) has previously been shown to affect atmospheric new particle formation; however, its role still remains highly uncertain. Through the employment of state-of-the-art quantum chemical methods, we study the (acid)1–2(base)1–2 and (acid)3(base)2 clusters containing at least one nitric acid (NA) and sulfuric acid (SA) or methanesulfonic acid (MSA) with bases ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). The initial cluster configurations are generated using the ABCluster program. PM7 and ωB97X-D/6-31++G(d,p) calculations are used to reduce the number of relevant configurations. The thermochemical parameters are calculated at the ωB97X-D/6-31++G(d,p) level of theory with the quasi-harmonic approximation, and the final single-point energies are calculated with high-level DLPNO–CCSD(T0)/aug-cc-pVTZ calculations. The enhancing effect from the presence of nitric acid on cluster formation is studied using the calculated thermochemical data and cluster dynamics simulations. We find that when NA is in excess compared with the other acids, it has a substantial enhancing effect on the cluster formation potential.

Influence of Generated Defects by Ar Implantation on the Thermoelectric Properties of ScN.

Nowadays, making thermoelectric materials more efficient in energy conversion is still a challenge. In this work, to reduce the thermal conductivity and thus improve the overall thermoelectric performances, point and extended defects were generated in epitaxial 111-ScN thin films by implantation using argon ions. The films were investigated by structural, optical, electrical, and thermoelectric characterization methods. The results demonstrated that argon implantation leads to the formation of stable defects (up to 750 K operating temperature). These were identified as interstitial-type defect clusters and argon vacancy complexes. The insertion of these specific defects induces acceptor-type deep levels in the band gap, yielding a reduction in the free-carrier mobility. With a reduced electrical conductivity, the irradiated sample exhibited a higher Seebeck coefficient while maintaining the power factor of the film. The thermal conductivity is strongly reduced from 12 to 3 W·m–1·K–1 at 300 K, showing the influence of defects in increasing phonon scattering. Subsequent high-temperature annealing at 1573 K leads to the progressive evolution of these defects: the initial clusters of interstitials evolved to the benefit of smaller clusters and the formation of bubbles. Thus, the number of free carriers, the resistivity, and the Seebeck coefficient are almost restored but the mobility of the carriers remains low and a 30% drop in thermal conductivity is still effective (ktotal ∼ 8.5 W·m–1·K–1). This study shows that control defect engineering with defects introduced by irradiation using noble gases in a thermoelectric coating can be an attractive method to enhance the figure of merit of thermoelectric materials.

Effect of cluster initiatives and natural clusters on business performance

PurposeThis study aims to determine how much company participation in a type of cluster affects its economic performance.Design/methodology/approachThis study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.FindingsResults show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.Research limitations/implicationsThis research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.Practical implicationsFrom the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.Originality/valueThis study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.

A marketing strategy to increase product sales of batik at micro and small enterprises

This article aims to identify the practice of Gotong Royong (mutual corporation culture; mutual help) marketing on batik products in Kampung Batik Laweyan. This study uses a quantitative descriptive method. The qualitative process is carried out with an exploratory study design. Data was collected by interviewing and observing batik entrepreneurs in Kampung Batik Laweyan (batik village of Solo, Central Java, Indonesia) to increase sales of batik products. Hypothesis testing uses a quantitative approach. Data collection was carried out by distributing questionnaires to 25 batik entrepreneurs in Kampung Batik Laweyan. This study found that batik entrepreneurs promoting Gotong Royong marketing carried out by good Laweyan Batik Village entrepreneurs were: building joint outlets, using the Laweyan Batik Village Website, conducting collective exhibitions, and collaborating between Batik Clusters in the city of Solo. The results show that the Gotong Royong marketing strategy can increase the profit of the batik business in Batik Laweyan village. The marketing strategy of cooperation to develop products in the Micro and Small Enterprise (MSEs) cluster is essential. The study results are expected to provide an overview and insight into knowledge about the culture of local wisdom in making business decisions. The Gotong Royong marketing strategy that is carried out can reduce the conventional marketing methods carried out individually by batik entrepreneurs.

The Tourist and Recreational Potential of Cross-Border Regions of Russia and Kazakhstan during the COVID-19 Pandemic: Estimation of the Current State and Possible Risks

The development of tourism is associated with numerous risks that have a direct and indirect impact on the realization of tourist and recreational potential. In recent years, in addition to internal risks, the importance of external environmental risks (geopolitical and epidemiological) has increased. The COVID-19 pandemic is one of the foremost of these risks, and its effects on the development of regional tourism demands attention. The purpose of the study is to estimate the level of tourist and recreational potential of cross-border regions of the Russian Federation and Kazakhstan, and the possible risks during the COVID-19 pandemic. After the breakup of the USSR, one of the longest land borders in the world was established between Russia and Kazakhstan. The geographical scope of the study includes 12 constituent entities of the Russian Federation and 7 regions of Kazakhstan. Information posted on statistical portals, data from geographical atlases, and specialized websites of the executive authorities were used as the materials for the study. The tourist and recreational potential of the regions of the Russian Federation and Kazakhstan was estimated by the scorecard method, with the assignment of weight coefficients to indicators included in four main clusters: Natural Factors, Cultural and Historical Factors, Social and Economic Factors, and Infrastructure Support of Tourism. Additionally, the experience of studying risks associated with tourism development during the pandemic was summarized. The conclusions reached are indicative of different levels of tourism and recreational potential in cross-border regions of the Russian Federation and Kazakhstan, and the inconsistency of the industry’s structure. It was found that the COVID-19 pandemic had increased the number of risks for the realization of tourism and recreational potential, which must be taken into account when making management decisions. The authorities of cross-border regions can use the results of the research to adjust tourism policy under the current restrictions and increased global risks. The application of mechanisms and methods of territorial planning and management will depend on the level of tourism and recreational potential. For regions with high and above-average potential, the emphasis should be on participation in federal projects, the development of cluster initiatives, and the application of a diversification strategy. Regions with medium and low potential should focus on the domestic tourist flow, develop inter-regional cooperation, and focus on the strategy of gaining a competitive advantage.

Improved BSO for ship path planning with grounding alert using interpolated Delaunay triangulation

ABSTRACT An improved depth-contour points generation algorithm under navigation safety constraints based on possible shallowest sounding and interpolation is proposed. An improved brain storm optimisation (BSO) for ship path planning with grounding alert using interpolated Delaunay triangulation is designed. The initial clustering centre selection method is improved to overcome the sensitivity of the initial value generated by the k-means algorithm. The clustering algorithm of the random walking centroid is used to improve the efficiency, stability, randomness, and diversity of the iterative process and prevents convergence to the local optimal solution. The tide height with grounding alert is considered in path planning to ensure that real-time water depth is deeper than the safe water depth threshold. The curve interpolation method is used for points protruding into shallow water, while the linear interpolation method is used for points protruding into deep water.

Articulatory Underpinnings of Reduced Acoustic-Phonetic Contrasts in Individuals With Amyotrophic Lateral Sclerosis.

The aim of this study is to identify the articulatory underpinnings of the acoustic-phonetic correlates of functional speech decline in individuals with amyotrophic lateral sclerosis (ALS). Thirteen individuals with varying severities of speech impairment secondary to ALS and 10 neurologically healthy controls speakers read 12 minimal word pairs, targeting the contrasts in the height, advancement, and length of vowels; the manner and place of articulation for consonants and consonant cluster; and liquid and glide approximants, 5 times. Sixteen acoustic features were extracted to characterize the phonetic contrasts of these minimal word pairs. These acoustic features were correlated with a functional speech index-intelligible speaking rate-using penalized regression, based on which the contributive features were identified as the acoustic-phonetic correlates of the functional speech outcome. Articulatory contrasts of the minimal word pairs were characterized by a set of dissimilarity indices derived by the dynamic time warping algorithm, which measured the differences in the displacement and velocity trajectories of tongue tip, tongue dorsum, lower lip, and jaw between the minimal word pairs. The contributive articulatory features to the acoustic-phonetic correlates were identified by penalized regression. A variety of acoustic-phonetic features were identified as contributing to the functional speech outcome, of which the contrasts in vowel height and advancement, [r]-[l], [r]-[w], and initial cluster-singleton were the most affected in individuals with ALS. Differential articulatory underpinnings were identified for these acoustic-phonetic features. Impairments of these articulatory underpinnings, especially of tongue tip and tongue dorsum velocities and tongue tip displacement, were associated with reduced acoustic-phonetic contrasts of the minimal word pairs, in a context-specific manner. The findings established explanatory relationships between articulatory impairment and the acoustic-phonetic profile of functional speech decline in ALS, providing useful information for developing targeted management strategies to improve and prolong functional speech in individuals with ALS.

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions depends largely on an accurate assessment of the distribution of urban hotspots (centers of traffic activity). In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. When the data dimensionality is large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering centers. To mitigate the problem, a hybrid heuristic “fuzzy system-particle swarm-genetic” algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI). A taxi GPS dataset and a multi-source dataset were employed to test and validate the effectiveness of the proposed algorithm in comparison to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K-Means, PSO based constraint K-Means, PSO based Weighted K-Means, PSO-GA based K-Means and K-Means++ algorithms. The comparison findings demonstrate that the proposed algorithm can achieve better clustering results, as well as successfully acquire urban hotspots.