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Many stability measures, such as Normalized Mutual Information (NMI), have been proposed to validate a set of partitionings. It is highly possible that a set of partitionings may contain one (or more) high quality cluster(s) but is still adjudged a bad cluster by a stability measure, and as a result, is completely neglected. Inspired by evaluation approaches measuring the efficacy of a set of partitionings, researchers have tried to define new measures for evaluating a cluster. Thus far, the measures defined for assessing a cluster are mostly based on the well-known NMI measure. The drawback of this commonly used approach is discussed in this paper, after which a new asymmetric criterion, called the Alizadeh-Parvin-Moshki-Minaei criterion (APMM), is proposed to assess the association between a cluster and a set of partitionings. We show that the APMM criterion overcomes the deficiency in the conventional NMI measure. We also propose a clustering ensemble framework that incorporates the APMM's capabilities in order to find the best performing clusters. The framework uses Average APMM (AAPMM) as a fitness measure to select a number of clusters instead of using all of the results. Any cluster that satisfies a predefined threshold of the mentioned measure is selected to participate in an elite ensemble. To combine the chosen clusters, a co-association matrix-based consensus function (by which the set of resultant partitionings are obtained) is used. Because Evidence Accumulation Clustering (EAC) can not derive the co-association matrix from a subset of clusters appropriately, a new EAC-based method, called Extended EAC (EEAC), is employed to construct the co-association matrix from the chosen subset of clusters. Empirical studies show that our proposed approach outperforms other cluster ensemble approaches. [ABSTRACT FROM AUTHOR]

One of the important aspects of decision making and management in distributed systems is collecting accurate information about the available resources of the peers. The previously proposed approaches for collecting such information completely depend on the system's architecture. In the server-oriented architecture, servers assume the main role of collecting comprehensive information from the peers and the system. Next, based on the information about the features of the basic activities and the system, an exact description of the peers' status is produced. Accurate decisions are then made using this description. However, the amount of information gathered in this architecture is too large, and it requires massive processing. On the other hand, updating the information takes time, causing delays and undermining the validity of the information. In addition, due to the limitations imposed by the servers, such architecture is not scalable and dynamic enough. The peer-to-peer architecture was introduced to address these concerns. However, due to a lack of complete knowledge of the peers and the system, the decisions are made without a precise description of the peers' status and are only based on the hardware data collected from the peers. Such an abstract and general image of the peers is not adequate for the purpose of decision making. In this paper, a 4-dimensional model is presented for the purpose of information collection and the exact description of the peer's status, including the features of the peer, the basic activity, the time, and the specifications of the system. The proposed model is for a server-oriented architecture, but it also adapts to the peer-to-peer serverless architecture. Based on this model, a new approach is introduced for information collection and an exact description of the peers' status in a peer-to-peer system based on the Latin square concept. We evaluate the model in the server-oriented and serverless situations. The workload is considered as the basic activity in our evaluation. Our evaluation demonstrates that in a server-oriented situation, increasing the size of the system has a direct relation with time. However, a serverless situation does not follow this behavior. [ABSTRACT FROM AUTHOR]

Abstract: Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets. [Copyright &y& Elsevier]

• A new method for big social data analysis to reveal traveller' behavior is presented. • EM clustering approach is used for customers' segmentation. • Neural network combined with fuzzy logic is used for preference prediction. • The method uses HOSVD for data dimensionality reduction. • The method is evaluated on eco-friendly hotels data. Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers' online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers' preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers' segmentation, and preference learning and prediction in eco-friendly hotels. [ABSTRACT FROM AUTHOR]