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It is essential to delve into the strategy of multimodal model pre-training, which is an obvious impact on downstream tasks. Currently, clustering learning has achieved noteworthy benefits in multiple methods. However, due to the availability of open image-text pairs, it is challenging for multimodal with clustering learning. In this paper, we propose an approach that utilizes clustering swap prediction strategy to learn image-text clustering embedding space by interaction prediction between image and text features. Unlike existing models with clustering learning, our method (Clus) allows for an open number of clusters for web-scale alt-text data. Furthermore, in order to train the image and text encoders efficiently, we introduce distillation learning approach and evaluate the performance of the image-encoder in downstream visual tasks. In addition, Clus is pre-trained end-to-end by using large-scale image-text pairs. Specifically, both text and image serve as ground truth for swap prediction, enabling effective representation learning. Concurrently, extensive experiments demonstrate that Clus achieves state-of-the-art performance on multiple downstream fine-tuning and zero-shot tasks (i.e., Image-Text Retrieval, VQA, NLVR2, Image Captioning, Object Detection, and Semantic Segmentation). [ABSTRACT FROM AUTHOR]

Shale reservoirs have very low original permeability and n o economic productivity under natural conditions. Practice shows that large hydraulic fracturing is the most effective means to develop shale reservoirs efficiently. However, there are few reports on the optimization of fracture cluster number in fractured horizontal w ells in shale reservoirs. In view of this problem, the multi-well numerical model of multi-stage fractured horizontal wells is established b y u sing the unstable seepage theory, and a set of optimization method of fracture cluster number of multi-stage fractured horizontal wells in shale reservoir is put forward on the principle of full utilization of reserves. The results show that when the number of fracture clusters in multi-stage fractured horizontal wells in Changji oilfield is more than 80,the cumulative increase trend of oil production tends to be flat. It can be determined that the reasonable cluster spacing range is about 15~20 m, and the number of fracture clusters is 70~ 80. According to the theoretical model with an optimal number of fracture clusters of 75,the cumulative oil production of fractured horizontal wells in shale reservoirs in Changji oilfield was calculated and found to be 25 150 m3 • This work can provide theoretical reference for the design of fracture clusters in fractured horizontal wells in shale reservoirs in Changji oilfield, Xinjiang. [ABSTRACT FROM AUTHOR]

To overcome the main problem of the cluster number in many clustering applications, a new clustering approach with improved morphology similarity distance and the novel cluster validity index is proposed in this paper. An optimized morphology similarity distance based on the Standard Euclidean distance and ReliefF algorithm is used to create a new validity index, which can balance the intra-cluster consistency and inter-cluster consistency. The proposed validity index is combined with fuzzy C-means to produce a creative algorithm simply named the OMS-OSC algorithm. Experimental results obtained using different artificial data sets and real-world data sets show that the new algorithm can not only yield good performance but also detect the correct cluster number. [ABSTRACT FROM AUTHOR]

The role of hydrogen bond interaction on the structural orientation of pure alkanolamines, monoethanolamine (MEA), diethanolamine (DEA), and methyl diethanolamine (MDEA), was under consideration by computer simulation, molecular dynamics simulation as well as density functional theory. Molecular dynamics simulations were carried out at different pressures and temperatures to discover structural stability. A comprehensive force field validation was performed to obtain accurate density. It was also observed that independent of pressure and temperature, MEA molecules prefer head-tail parallel orientation, DEA molecules show both vertical and parallel orientation, and MDEA molecules favor parallel arrangement in liquid state. The probability of hydrogen bond as a substantial intermolecular interaction was also investigated and substantial sites were evaluated. Intermolecular and intramolecular energy, hydrogen bond strength, and transport properties, as well as cluster formation, were investigated to offer an irrefutable proof on structural orientation. Ab initio computation by analyzing frequency, stabilization energy, and electron location function confirmed similar orientation, proposed by molecular dynamics simulation. [ABSTRACT FROM AUTHOR]

The Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has shown the ability to model nonlinear system at high accuracy. The initial Fuzzy Inference System (FIS) is required before ANFIS parameters' training. The generation of initial FIS requires the number of inputs, number of clusters and cluster centers to be determined. Instead of determining the above parameters by heuristic approach, the number and variable of inputs of an Electro-Hydraulic Actuator (EHA) system are determined through mathematical derivation of the system. The number of clusters for each input is determined via fuzzy gap statistic, which applies Fuzzy C-means (FCM) instead of k-means of gap statistic in calculating the within cluster dispersion. FCM clustering also provides the center of each cluster identified. Information of the input variables and cluster numbers as well as cluster centers are used to obtain initial FIS. ANFIS hybrid training algorithm is used to train the initial FIS for parameters. The result shows that the model obtained using ANFIS approach can estimate the EHA's system response at high precision. [ABSTRACT FROM AUTHOR]

In cluster analysis, finding the number of clusters, K, for a given dataset is an important yet very tricky task, simply for the facts that there is no universally accepted correct or wrong answer for most real world problems and it all depends on the context and purpose of a cluster study. Numerous methods have been developed for estimating K, but most are not widely used in practice due to their poor performance. Thus, it is still quite common that human user is required to select a specific value or a range for K for many clustering methods before they are used. Inappropriate predetermination for K can result in poor clustering results. This paper presents a new method for estimating the most probable number of clusters automatically. It firstly calculates the length of constant similarity intervals, L, and then considers the longest ones as the representations of the most probable numbers of the clusters under the set context and the chosen similarity measure. An error function is defined to measure and evaluate the goodness of estimations. The proposed method has been tested on 3 synthetic datasets and 8 real-world benchmark datasets, and compared with some other popular methods including particularly the TwoStep implemented in IBM/SPSS Modeler software package. The experimental results showed that the proposed method is able to find the "desired" predominant number of clusters for all the simulated datasets and most of the benchmark datasets, and the statistical tests indicate that our method is significantly better. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new kind of -means algorithms for clustering analysis with three frequency sensitive (data) discrepancy metrics in the cases that the exact number of clusters in a dataset is not pre-known. That is, by setting the number k of seed-points for learning clusters to be larger than the true number of actual clusters in the dataset, i.e., , these algorithms can locate the centers of actual clusters by converged seed-points, respectively, with the extra seed-points corresponding to empty clusters, namely containing no winning points in the competition according to the underlying frequency sensitive discrepancy metrics. It is demonstrated by the experiments on both synthetic and real-world datasets that these three new -means clustering algorithms can detect the number of actual clusters in a dataset with a classification accuracy rate as high as or higher than that of the original -means algorithm. Moreover, they converge more quickly than the original one. [Copyright &y& Elsevier]

Abstract: This paper introduces k′-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm. [Copyright &y& Elsevier]

The existing Rival Penalized Competitive Learning (RPCL) algorithm and its variants have provided an attractive way to perform data clustering without knowing the exact number of clusters. However, their performance is sensitive to the preselection of the rival delearning rate. In this paper, we further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically. Consequently, we propose the Rival Penalization Controlled Competitive Learning (RPCCL) algorithm and its stochastic version. In each of these algorithms, the selection of the delearning rate is circumvented using a novel technique. We compare the performance of RPCCL to RPCL in Gaussian mixture clustering and color image segmentation, respectively. The experiments have produced the promising results. [ABSTRACT FROM AUTHOR]

Expectation-Maximization (EM) algorithm [10] has been extensively used in density mixture clustering problems, but it is unable to perform model selection automatically. This paper, therefore, proposes to learn the model parameters via maximizing a weighted likelihood. Under a specific weight design, we give out a Rival Penalized Expectation-Maximization (RPEM) algorithm, which makes the components in a density mixture compete each other at each time step. Not only are the associated parameters of the winner updated to adapt to an input, but also all rivals' parameters are penalized with the, strength proportional to the corresponding posterior density probabilities. Compared to the EM algorithm [10], the RPEM is able to fade out the redundant densities from a density mixture during the learning process. Hence, it can automatically select an appropriate number of densities in density mixture clustering. We experimentally demonstrate its outstanding performance on Gaussian mixtures and color image segmentation problem. Moreover, a simplified version of RPEM generalizes our recently proposed RPCCL algorithm [8] so that it is applicable to elliptical clusters as well with any input proportion. Compared to the existing heuristic RPCL [25] and its variants, this generalized RPCCL (G-RPCCL) circumvents the difficult preselection of the so-called delearning rate. Additionally, a special setting of the G-RPCCL not only degenerates to RPCL and its Type A variant, but also gives a guidance to choose an appropriate delearning rate for them. Subsequently, we propose a stochastic version of RPCL and its Type A variant, respectively, in which the difficult selection problem of delearning rate has been novelly circumvented. The experiments show the promising results of this stochastic implementation. [ABSTRACT FROM AUTHOR]

This paper presents a generalized version of the conventional k-means clustering algorithm [Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, University of California Press, Berkeley, 1967, p. 281]. Not only is this new one applicable to ellipse-shaped data clusters without dead-unit problem, but also performs correct clustering without pre-assigning the exact cluster number. We qualitatively analyze its underlying mechanism, and show its outstanding performance through the experiments. [Copyright &y& Elsevier]