Shape Of Clusters Research Articles

Effect of Planar Interfaces on Nucleation in Melting and Crystallization.

The effect of planar interfaces on nucleation (namely, on the work of critical cluster formation and their shape) is studied both for crystallization and melting. Advancing an approach formulated about 150 years ago by J. W. Gibbs for liquid phase formation at planar liquid–liquid interfaces, we show that nucleation of liquids in the crystal at crystal–vapor planar interfaces proceeds as a rule with a much higher rate compared to nucleation in the bulk of the crystal. Provided the surface tensions crystal–liquid (), liquid–vapor (), and crystal–vapor () obey the condition , the work of critical cluster formation tends to zero; in the range , it is less than one half of the work of critical cluster formation for bulk nucleation. The existence of a liquid–vapor planar interface modifies the work of critical cluster formation in crystal nucleation in liquids to a much less significant degree. The work of critical crystal cluster formation is larger than one half of the bulk value of the work of critical cluster formation, reaching this limit at . The shape of the critical clusters can be described in both cases by spherical caps with a radius, R, and a width parameter, h. This parameter, h, is the distance from the cutting plane (coinciding with the crystal–vapor and liquid–vapor planar interface, respectively) to the top of the spherical cap. It varies for nucleation of a liquid in a crystal in the range and for crystal nucleation in a liquid in the range . At , the ratio of the critical cluster for nucleation in melting tends to zero (). At the same condition, the critical crystallite has the shape of a sphere located tangentially to the liquid–vapor interface inside the liquid (). We present experimental data which confirm the results of the theoretical analysis, and potential further developments of the theoretical approach developed here are anticipated.

Fusing attention mechanism with Mask R-CNN for instance segmentation of grape cluster in the field.

Accurately detecting and segmenting grape cluster in the field is fundamental for precision viticulture. In this paper, a new backbone network, ResNet50-FPN-ED, was proposed to improve Mask R-CNN instance segmentation so that the detection and segmentation performance can be improved under complex environments, cluster shape variations, leaf shading, trunk occlusion, and grapes overlapping. An Efficient Channel Attention (ECA) mechanism was first introduced in the backbone network to correct the extracted features for better grape cluster detection. To obtain detailed feature map information, Dense Upsampling Convolution (DUC) was used in feature pyramid fusion to improve model segmentation accuracy. Moreover, model generalization performance was also improved by training the model on two different datasets. The developed algorithm was validated on a large dataset with 682 annotated images, where the experimental results indicate that the model achieves an Average Precision (AP) of 60.1% on object detection and 59.5% on instance segmentation. Particularly, on object detection task, the AP improved by 1.4% and 1.8% over the original Mask R-CNN (ResNet50-FPN) and Faster R-CNN (ResNet50-FPN). For the instance segmentation, the AP improved by 1.6% and 2.2% over the original Mask R-CNN and SOLOv2. When tested on different datasets, the improved model had high detection and segmentation accuracy and inter-varietal generalization performance in complex growth environments, which is able to provide technical support for intelligent vineyard management.

A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules

This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and output spaces. However, the prediction performance decreases when the specific data points with geometric features are used. The CGK clustering solves the above situation by generating valid clusters considering the geometric attributes of data in input and output spaces with the aid of the Mahalanobis distance. However, it is necessary to generate rational information granules (IGs) because there is a significant change in performance according to the context generated in the output space and the shape, size, and several clusters generated in the input space. As a result, the rational IGs are obtained by considering the relationship between the coverage and specificity of IG using the genetic algorithm (GA). Thus, the optimized CGK-based fuzzy granular models with the aggregated structure are designed based on rational IGs. The prediction performance was compared using the two databases to verify the validity of the proposed method. Finally, the experiments revealed that the performance of the proposed method is higher than that of the previous model.

Molecular simulation on CO2 adsorption in partially water-saturated kaolinite nanopores in relation to carbon geological sequestration

CO2 geological sequestration in depleted gas/oil reservoirs is regarded as a promising strategy to mitigate CO2 emission thanks to the well-developed nanoscale pore structures providing substantial adsorption spaces. Therefore, the fundamental understanding of CO2 adsorption in shale nanopores can provide important insights into geological CO2 sequestration. In this work, we use molecular dynamics (MD) and Grand canonical Monte Carlo (GCMC) simulations to investigate CO2 adsorption in kaolinite nanopores with two different basal surfaces and study moisture effect. In the absence of water, CO2 presents stronger adsorption in gibbsite nanopores than siloxane nanopores. In gibbsite pores, water tends to spread over the surfaces forming thin water films. While CO2 is driven to the middle of the pore, an enrichment of CO2 is observed at the water-CO2 interface. On the other hand, water bridges form in siloxane pores. In siloxane mesopores, the shape of water clusters gradually turns to be spherical ones as pressure increases suggesting a more CO2-wet surface, while the deformation of water clusters in micropores is not obvious due to stronger confinement effects. The CO2 distributions in siloxane mesopores can be divided into six zones. The highest CO2 density appears in the three-phase contact areas, while CO2 has a high tendency to accumulate in two-phase contact areas. In general, the presence of water results in the reduction of CO2 adsorption in both gibbsite and siloxane pores. Overall, water has a more detrimental influence on CO2 adsorption in gibbsite pores than siloxane pores. Our work should provide important insights into CO2 adsorption in kaolinite nanopores and reveal CO2 adsorption mechanisms in the presence of water.

Momentum signatures of site percolation in disordered two-dimensional ferromagnets

In this work, we consider a two-dimensional square lattice of pinned magnetic spins with nearest-neighbour interactions and we randomly replace a fixed proportion of spins with nonmagnetic defects carrying no spin. We focus on the linear spin-wave regime and address the propagation of a spin-wave excitation with initial momentum $k_0$. We compute the disorder-averaged momentum distribution obtained at time $t$ and show that the system exhibits two regimes. At low defect density, typical disorder configurations only involve a single percolating magnetic cluster interspersed with single defects essentially and the physics is driven by Anderson localization. In this case, the momentum distribution features the emergence of two known emblematic signatures of coherent transport, namely the coherent backscattering (CBS) peak located at $-k_0$ and the coherent forward scattering (CFS) peak located at $k_0$. At long times, the momentum distribution becomes stationary. However, when increasing the defect density, site percolation starts to set in and typical disorder configurations display more and more disconnected clusters of different sizes and shapes. At the same time, the CFS peak starts to oscillate in time with well defined frequencies. These oscillation frequencies represent eigenenergy differences in the regular, disorder-immune, part of the Hamiltonian spectrum. This regular spectrum originates from the small-size magnetic clusters and its weight grows as the system undergoes site percolation and small clusters proliferate. Our system offers a unique spectroscopic signature of cluster formation in site percolation problems.

Study the silica content of paddy husks from different varieties of paddy cultivated in North Western Province, Sri Lanka

Rice husk is one of the main agricultural waste available in Sri Lanka. They are the outer covering of rice grains. Once the paddy is separated from rice grain, the husk is removed from rest of the grain. This study show depending on rice husk variety moisture content, average bulk density, amount of precipitated silica varies. Further EDX analysis show depending on variety percentage of Oxygen, Silicon and other contaminants are varied. Eight different rice husk varieties available in North Western Province is studied in this research. Moisture Content (MC), average bulk density and silica content in each variety were initially calculated. Moisture content varies between 10.42% to 12.84%. Average bulk density varies between 214.42 to 268.57 . Highest amount of silica was obtained as 98.2% from Murugakanayan rice variety. Lowest amount of silica was obtained as 74% from BG 360 rice variety. A typical XRD, SEM patterns and EDX was done to silica samples obtained from rice husk ash. Amorphous nature of the obtained silica was confirmed from XRD spectra. There is a broad peak with maximum intensity at 2Ө = 22 ͦ in XRD spectra. SEM images have shown that these silica particles were aggregated as irregular rod shape clusters. EDX images confirm high amount of sample weight contain silicon and oxygen. Yet very less amount of sodium is available in the sample.

Phosphonated Lower-Molecular-Weight Polyethyleneimines as Oilfield Scale Inhibitors: An Experimental and Theoretical Study

For many years, amino methylenephosphonate (-CH2-N-PO3H2)-based scale inhibitors (SIs) have been deployed for preventing various scales in the oil and gas industry, particularly for squeeze treatment applications. However, this class of phosphonate inhibitors showed several limitations related to environmental concerns and compatibility with brine solutions. The low toxicity of low-molecular-weight polyethyleneimine (LMW-PEI) encouraged us to phosphonate a series of branched and linear PEIs via the Moedritzer–Irani reaction. The phosphonated polyethyleneimine PPEIs are branched PPEI-600, branched PPEI-1200, branched PPEI-2000, and linear PPEI-5000. The newly synthesized PPEIs (branched and linear) were screened for calcium carbonate and barium sulfate utilizing a high-pressure dynamic tube-blocking rig at 100 °C and 80 bar. Moreover, we report the compatibility activity of all PPEIs with various concentrations of calcium ions (up to 10000 ppm). The morphology of the calcium carbonate and barium sulfate scale crystals in the absence and presence of linear PPEI-5000 was also investigated under static conditions using scanning electron microscopy (SEM). The obtained results showed that all branched and linear PPEIs gave moderate calcite and barite inhibition activities. It was also found that all branched PPEIs gave moderate to poor calcium compatibility at high dosages of calcium ions (1000–10 000 ppm). Interestingly, linear PPEI-5000 displayed superior compatibility properties at high dosages of SI (up to 50 000 ppm) and high concentrations of Ca2+ ions (up to 10 000 ppm). Furthermore, field emission scanning electron microscopy analysis confirmed that the crystal shapes of CaCO3 and BaSO4 mineral scales are greatly changed in the presence of linear PPEI-5000. At high dosages of linear PPEI-5000 SI (100 ppm), the CaCO3 crystals are completely converted from cubic-shaped blocks (blank calcite) into long cluster shapes. Density functional theory (DFT) simulations reveal favorable interactions of PPEI polymers with the two mineral facets (calcite and barite) with more affinity toward the calcite surface. PPEI with more phosphonate groups exhibits affinities comparable to the commercial-scale inhibitors. The high density of the phosphonate groups on the branched PPEI and its strong affinity toward calcium ions explain its poor calcium compatibility. The polymer flocculation and sluggish barite kinetics are the potential reasons for its low performance against the barite scale.

Triaxiality in galaxy clusters: Mass versus potential reconstructions

Context. Accounting for the triaxial shapes of galaxy clusters will become important in the context of upcoming cosmological surveys. This will provide a challenge given that the density distribution of gas cannot be described by simple geometrical models without loss of information. Aims. We investigate the effects of simple 3D models on cluster gravitational potentials and gas density distribution to determine which of these quantities is most suitable and appropriate for characterising galaxy clusters in cosmological studies. Methods. We use a statistical sample of 85 galaxy clusters from a large cosmological N-body + hydrodynamical simulation to investigate cluster shapes as a function of radius for both gas density and potential. We examine how the resulting parameters are affected by the substructure removal (for the gas density) and by the definition of the computation volume (interior vs. shells). Results. We find that the orientation and axis ratio of gas isodensity contours are degenerate with the presence of substructures and are unstable against fluctuations. Moreover, as the derived cluster shape depends on the method used for removing the substructures, thermodynamic properties extracted from the X-ray emissivity profile, for example, suffer from this additional and often underestimated bias. In contrast, the shapes of the smooth cluster potentials are less affected by fluctuations and converge towards simple geometrical models, both in the case of relaxed and dynamically active clusters. Conclusions. The observation that cluster potentials can be represented better by simple geometrical models and reconstructed with a lower level of systematic error for both dynamically active and relaxed clusters suggests that characterising galaxy clusters by their potential is a promising alternative to using cluster masses in cluster cosmology. With this approach, dynamically active and relaxed clusters could be combined in cosmological studies, improving statistics and lowering scatter.

RFDPC: Density Peaks Clustering Algorithm Based on Resultant Force

Density peaks clustering (DPC) is an efficient and effective algorithm due to its outstanding performance in discovering clusters with varying densities. However, the quality of this method is highly dependent on the cutoff distance. To improve the performance of DPC, the gravitation-based clustering (GDPC) algorithm is proposed. However, it cannot identify the clusters of varying densities. We developed a novel density peaks clustering algorithm based on the magnitude and direction of the resultant force acting on a data point (RFDPC). RFDPC is based on the idea that the resultant forces acting on the data points in the same cluster are more likely to point towards the cluster center. The cluster centers are selected based on the force directional factor and distance in the decision graph. Experimental results indicate superior performance of the proposed algorithm in detecting clusters of different densities, irregular shapes, and numbers of clusters.

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.

Controlling the Shape of Small Clusters with and without Macroscopic Fields.

Despite major advances in the understanding of the formation and dynamics of nanoclusters in the past decades, theoretical bases for the control of their shape are still lacking. We investigate strategies for driving fluctuating few-particle clusters to an arbitrary target shape in minimum time with or without an external field. This question is recast into a first passage problem, solved numerically, and discussed within a high temperature expansion. Without field, large-enough low-energy target shapes exhibit an optimal temperature at which they are reached in minimum time. We then compute the optimal way to set an external field to minimize the time to reach the target, leading to a gain of time that grows when increasing cluster size or decreasing temperature. This gain can shift the optimal temperature or even create one. Our results could apply to clusters of atoms at equilibrium, and colloidal or nanoparticle clusters under thermo- or electrophoresis.

The Myotube Analyzer: how to assess myogenic features in muscle stem cells

BackgroundThe analysis of in vitro cultures of human adult muscle stem cells obtained from biopsies delineates the potential of skeletal muscles and may help to understand altered muscle morphology in patients. In these analyses, the fusion index is a commonly used quantitative metric to assess the myogenic potency of the muscle stem cells. Since the fusion index only partly describes myogenic potency, we developed the Myotube Analyzer tool, which combines the definition of the fusion index with extra features of myonuclei and myotubes obtained from satellite cell cultures.ResultsThe software contains image adjustment and mask editing functions for preprocessing and semi-automatic segmentation, while other functions can be used to determine the features of nuclei and myotubes. The fusion index and a set of five novel parameters were tested for reliability and validity in a comparison between satellite cell cultures from children with cerebral palsy and typically developing children. These novel parameters quantified extra nucleus and myotube properties and can be used to describe nucleus clustering and myotube shape. Two analyzers who were trained in cell culture defined all parameters using the Myotube Analyzer app. Out of the six parameters, five had good reliability reflected by good intra-class correlation coefficients (> 0.75). Children with cerebral palsy were significantly different from the typically developing children (p < 0.05) for five parameters, and for three of the six parameters, these differences exceeded the minimal detectable differences.ConclusionsThe Myotube Analyzer can be used for the analysis of fixed differentiated myoblast cultures with nuclear and MyHC staining. The app can calculate the fusion index, an already existing parameter, but also provides multiple new parameters to comprehensively describe myogenic potential in its output. The raw data used to determine these parameters are also available in the output. The parameters calculated by the tool can be used to detect differences between cultures from children with cerebral palsy and typically developing children. Since the program is open source, users can customize it to fit their own analysis requirements.

Bayesian hierarchical mixture models for detecting non‐normal clusters applied to noisy genomic and environmental datasets

SummaryClustering to find subgroups with common features is often a necessary first step in the statistical modelling and analysis of large and complex datasets. Although follow‐up analyses often make use of complex statistical models that are appropriate for the specific application, most popular clustering approaches are either nonparametric, or based on Gaussian mixture models and their variants, often for reasons of computational efficiency. Certain characteristics in the data, such as the presence of outliers, or non‐ellipsoidal cluster shapes, that are common in modern scientific datasets, often lead these methods to fail to detect the cluster components accurately. In this article, we present two efficient and robust Bayesian clustering approaches that seek to overcome these limitations—a model‐based ‘tight’ clustering approach to cluster points in the presence of outliers, and a hierarchical Laplace mixture‐based approach to cluster heavy‐tailed and otherwise non‐normal cluster components—and illustrate their power and accuracy in detecting meaningful clusters in datasets from genomics, imaging and the environmental sciences.

A Framework of Mahalanobis-Distance Metric With Supervised Learning for Clustering Multipath Components in MIMO Channel Analysis

As multipath components (MPCs) are experimentally observed to appear in clusters, cluster-based channel models have been focused in the wireless channel study. However, most of the MPC clustering algorithms for MIMO channels with delay and angle information of MPCs are based on the distance metric that quantifies the similarity of two MPCs and determines the preferred cluster shape, greatly impacting MPC clustering quality. In this paper, a general framework of Mahalanobis-distance metric is proposed for MPC clustering in MIMO channel analysis, without user-specified parameters. Remarkably, the popular multipath component distance (MCD) is proved to be a special case of the proposed distance metric framework. Furthermore, two machine learning algorithms, namely, weak-supervised Mahalanobis metric for clustering and supervised large margin nearest neighbor, are introduced to learn the distance metric. To evaluate the effectiveness, a modified channel model is proposed based on the 3GPP spatial channel model to generate clustered MPCs with delay and angular information, since the original 3GPP spatial channel model (SCM) is incapable to evaluate clustering quality. Experiment results show that the proposed distance metric can significantly improve the clustering quality of existing clustering algorithms, while the learning phase requires considerably limited efforts of labeling MPCs.

Biosynthesis of copper nanoparticles using symbiotic bacterium Xenorhabdus sp, isolated from entomopathogenic nematode and its antimicrobial and insecticidal activity against Spodoptera litura

The field bio-nanotechnology has provided insight in creation of biosynthetic nanomaterials. Nanoparticles synthesis by entomopathogenic microbes are considered as the novel methods and gained worldwide attention in the field of pest control. This study focused on the synthesis and characterization of CuNPs using Xenorhabdus sp, and their antimicrobial and larvicidal activities with potted plant experiments. 335 nm λmax at UV-Vis spec indicates the presence of CuNPs and functional groups such as alcohols and phenols were confirmed. SEM revealed the clustered shape. EDaX confirms the composition of copper. Crystalline nature were observed in XRD. TEM indicates the particles ranging from 5 to 20nm.The antimicrobial activity against E. coli, P. aeruginosa and Fusarium sp, shows higher zone of inhibition. Larvicidal activity of S. litura shows highest mortality. In potted plant experiment, less damage was observed in CuNPs. Our experiment concludes that, CuNPs synthesized, has highest insecticidal and antimicrobial activity, which can be used in biopesticides.

Detection of space-time clusters using a topological hierarchy for geospatial data on COVID-19 in Japan.

In this paper, we detected space–time clusters using data on coronavirus disease 2019 (COVID-19) collected daily by each prefecture in Japan. COVID-19 has spread globally since the first confirmed case in China, in December 2019. Several people have to date been infected in Japan since the first confirmed case in January 2020. The outbreak of COVID-19 has had a significant impact on many people’s lives. Studies are being conducted to detect regions, called clusters, which pose a significantly higher risk of infection than their surrounding areas, based on a spatial scan statistics of COVID-19 infections. Among these studies, space–time cluster detection has to date been actively performed to gain knowledge regarding infection status. Based on the spatial scan statistic, the cylindrical scan method is a widely used space–time cluster detection method. This method enables concurrent detection of the location and time of a cluster occurrence. However, this method cannot capture spatial changes in a cluster over time. When applying the existing method to a cluster whose shape changes over time, the number of calculations required becomes extremely large, and the analysis may become difficult. In this study, we focused on the hierarchical structure of the data obtained by conducting an echelon analysis and applied the space–time cluster detection method based on this structure to enable the capture of changes in a cluster’s shape. Furthermore, we visualized the location and period of a cluster’s occurrence and considered the cause of the cluster.

BiFlowAMOEBA for the identification of arbitrarily shaped clusters in bivariate flow data

A bivariate flow cluster is a group of two types of spatial flows, where both types of flows have high (or low) values, or one type of flow has a high value while the other has a low value. Identifying bivariate flow clusters aids in understanding the complex interactions between different flow patterns. Detecting bivariate flow clusters remains challenging because statistics for quantitatively assessing bivariate flow clusters are lacking and the shapes and sizes of clusters vary. This study proposes a novel bivariate flow clustering method (BiFlowAMOEBA) by improving a multidirectional optimum ecotope-based algorithm (AMOEBA) which embeds local Getis-Ord statistic in an iterative procedure to detect irregular-shaped clusters. We define a bivariate local Getis-Ord statistic for quantitatively assessing bivariate flow clusters, use a hierarchical clustering strategy to construct clusters, and evaluate the statistical significance of clusters using a Monte Carlo simulation. Experimental results of simulated datasets show that BiFlowAMOEBA can identify bivariate flow clusters of different shapes more accurately and completely, compared with two state-of-the-art methods. Two case studies show that BiFlowAMOEBA helps not only unveil the interactions between public transport and taxi services but also identifies competition patterns between taxis and ride-hailing services.

Dynamical state of galaxy clusters evaluated from X-ray images

ABSTRACT X-ray images of galaxy clusters often show disturbed structures that are indications of cluster mergers. To complement our previous work on the dynamical state of 964 clusters observed by Chandra, we process the X-ray images for 1308 clusters from XMM–Newton archival data, together with the images of 22 clusters newly released by Chandra, and we evaluate their dynamical state from these X-ray images. The concentration index c, the centroid shift ω and the power ratio P3/P0 are calculated in circular regions with a certain radius of 500 kpc, and the morphology index δ is estimated within elliptical regions that can be adapted to the cluster size and shape. In addition, the dynamical parameters for 42 clusters previously estimated from Chandra images are upgraded based on the newly available redshifts. Good consistence is found between dynamical parameters derived from XMM–Newton and Chandra images for the overlapped sample of clusters in the two data sets. The dependence of mass scaling relations on the dynamical state is shown by using the data of 388 clusters. All data and related software are available at http://zmtt.bao.ac.cn/galaxy_clusters/dyXimages/.

A fast DBSCAN algorithm for big data based on efficient density calculation

Today, data is being generated with a high speed. Managing large volume of data has become a challenge in the current age. Clustering is a method to analyze data that is generated in the Internet. Various approaches have been presented for data clustering until now. Among them, DBSCAN is a most well-known density-based clustering algorithm. This algorithm can detect clusters of different shapes and does not require prior knowledge about the number of clusters. A major part of the DBSCAN run-time is spent to calculate the distance of data from each other to find the neighbors of each sample in the dataset. The time complexity of this algorithm is O(n2); Therefore, it is not suitable for processing big datasets.In this paper, DBSCAN is improved so that it can be applied to big datasets. The proposed method calculates accurately each sample density based on a reduced set of data. This reduced set is called the operational set. This collection is updated periodically. The use of local samples to calculate the density has greatly reduced the computational cost of clustering. The empirical results on various datasets of different sizes and dimensions show that the proposed algorithm increases the clustering speed compared to recent related works while having similar accuracy as the original DBSCAN algorithm.

Enhancement of BIRCH Algorithm for Clusters of Different Shapes

BIRCH algorithm uses the concept of radius to manage cluster boundaries, which yields good results when clustering spherical data but unsatisfactory results when clustering non-spherical data. In which, it splits non-spherical data into several clusters in some circumstances. This paper proposed an enhancement to the BIRCH algorithm that allows it to cluster non-spherical data. The result of the experiment in this paper shows Enhanced BIRCH algorithm performs better than BIRCH algorithm in clustering non-spherical dataset.