As market competition intensifies, customer churn management is increasingly becoming an important means of competitive advantage for companies. However, when dealing with big data in the industry, ...existing churn prediction models cannot work very well. In addition, decision makers are always faced with imprecise operations management. In response to these difficulties, a new clustering algorithm called semantic-driven subtractive clustering method (SDSCM) is proposed. Experimental results indicate that SDSCM has stronger clustering semantic strength than subtractive clustering method (SCM) and fuzzy c-means (FCM). Then, a parallel SDSCM algorithm is implemented through a Hadoop MapReduce framework. In the case study, the proposed parallel SDSCM algorithm enjoys a fast running speed when compared with the other methods. Furthermore, we provide some marketing strategies in accordance with the clustering results and a simplified marketing activity is simulated to ensure profit maximization.
In the above article <xref ref-type="bibr" rid="ref1">1 , one reference <xref ref-type="bibr" rid="ref2">2 was missing. This reference should be inserted between the original references 9 and 10, ...that is, the order of the reference 10 and subsequent references increases by one.
Subspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent ...representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points using original features, our method explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation. Using the complementarity of multiple views, the latent representation depicts data more comprehensively than each individual view, accordingly making subspace representation more accurate and robust. We proposed two LMSC formulations: linear LMSC (lLMSC), based on linear correlations between latent representation and each view, and generalized LMSC (gLMSC), based on neural networks to handle general relationships. The proposed method can be efficiently optimized under the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework. Extensive experiments on diverse datasets demonstrate the effectiveness of the proposed method.
The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades, following the worldwide-seeking-goal of a ...carbon-neutral economy and the country’s incentives in diversifying its generation mix. From a long-term perspective, the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years. In this context, the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil. Leveraging on unsupervised Machine Learning methods, we focus on identifying similar days over the years, Representative Days, that can depict the fundamental underlying behaviour of each source. The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country: three in the Northeast and one in the South Regions, for wind speed; and three in the Northeast and one in the Southeast Regions, for solar irradiance. We use two Partitioning Methods (K-Means and K-Medoids), the Hierarchical Ward’s Method, and a Model-Based Method (Self-Organizing Maps). We identified that wind speed and solar irradiance can be effectively represented, respectively, by only two representative days, and two or three days, depending on the region and method (segments data with respect to the intensity of each source). Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target one. Mainstream UDA ...methods learn aligned features between the two domains, such that a classifier trained on the source features can be readily applied to the target ones. However, such a transferring strategy has a potential risk of damaging the intrinsic discrimination of target data. To alleviate this risk, we are motivated by the assumption of structural domain similarity, and propose to directly uncover the intrinsic target discrimination via discriminative clustering of target data. We constrain the clustering solutions using structural source regularization that hinges on our assumed structural domain similarity. Technically, we use a flexible framework of deep network based discriminative clustering that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one; replacing the auxiliary distribution with that formed by ground-truth labels of source data implements the structural source regularization via a simple strategy of joint network training. We term our proposed method as Structurally Regularized Deep Clustering (SRDC), where we also enhance target discrimination with clustering of intermediate network features, and enhance structural regularization with soft selection of less divergent source examples. Careful ablation studies show the efficacy of our proposed SRDC. Notably, with no explicit domain alignment, SRDC outperforms all existing methods on three UDA benchmarks.
This paper investigates fundamental limits of exact recovery in the general <inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula>-uniform hypergraph stochastic block model ...(<inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula>-HSBM), wherein <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> nodes are partitioned into <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> disjoint communities with relative sizes <inline-formula> <tex-math notation="LaTeX">(p_{1},\ldots , p_{k}) </tex-math></inline-formula>. Each subset of nodes with cardinality <inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula> is generated independently as an order-<inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula> hyperedge with a certain probability that depends on the ground-truth communities that the <inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula> nodes belong to. The goal is to exactly recover the <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> hidden communities based on the observed hypergraph. We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below the threshold (apart from a small regime of parameters that will be specified precisely). This threshold is represented in terms of a quantity which we term as the generalized Chernoff-Hellinger divergence between communities. Our result for this general model recovers prior results for the standard SBM and <inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula>-HSBM with two symmetric communities as special cases. En route to proving our achievability results, we develop a polynomial-time two-stage algorithm that meets the threshold. The first stage adopts a certain hypergraph spectral clustering method to obtain a coarse estimate of communities, and the second stage refines each node individually via local refinement steps to ensure exact recovery.
In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach is presented for solving the large-scale decision making (LSDM) problem. It consists of two algorithms: ...the sparse representation-based intuitionistic fuzzy clustering-exactly precision algorithm (which is presented for an exactly precision requirement), and the sparse representation-based intuitionistic fuzzy clustering-soft precision and scalable algorithm (which is proposed for soft precision and scalable requirements). In the proposed SRIFC approach, decision makers are clustered into several interest groups according to their interest preferences and relation sparsity of their intuitionistic fuzzy assessment information. The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process. According to the illustrative experiment results, the presented SRIFC approach is an adaptive and the unsupervised clustering method and presents more robust and efficient for LSDM problems.
In this article, an effective hybrid method is proposed to design the subarrays generating multiple functional beams. In the process of synthesis, one of the target patterns is selected as the ...primary pattern which is generated at the element level. Other secondary patterns are generated at the subarray level by dividing the elements into nonoverlapped subarrays and utilizing attenuators and phase shifters at the subarray outputs. To simplify the complexity of the feed network, the subarray configurations of secondary patterns are the same. The proposed method consists of two steps. In the first step, the subarray configuration of the phased array is optimized by the <inline-formula> <tex-math notation="LaTeX">{K} </tex-math></inline-formula>-means clustering method. In the second step, the convex optimization method is applied to acquiring the complex weighting coefficients of subarrays, which can improve the accuracy of the synthesized secondary patterns. A series of numerical examples demonstrate the effectiveness of the proposed method.
In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps to understand the subpatterns of the ...data that can be used to infer user's behaviors and identify potential risks. Due to the complexity of human behaviors and changing social environments, the distributions of financial data are usually complex and it is challenging to find clusters and give reasonable interpretations. The goal of this study is to develop an integrated approach to detect clusters in financial data, and optimize the scope of the clusters such that the clusters can be easily interpreted. Specifically, we first proposed a new cluster quality evaluation criterion, which is free from large-scale computation and can guide base clustering algorithms such as <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-Means to detect hyperellipsoidal clusters adaptively. Then, we designed a new solver for a revised support vector data description model, which efficiently refines the centroids and scopes of the detected clusters to make the clusters tighter such that the data in the clusters share greater similarities, and thus, the clusters can be easily interpreted with eigenvectors. Using ten financial datasets, the experiments showed that the proposed algorithm can efficiently find reasonable number of clusters. The proposed approach is suitable for large-scale financial datasets whose features are meaningful, and also applicable to financial mining tasks, such as data distribution interpretation and anomaly detection.
The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A ...five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart grid environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented.
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