Intelligent Data Analysis - Volume 19, issue 3

Article Type: Editorial

DOI: 10.3233/IDA-150726

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 469-470, 2015

Authors: Zhu, Bing | He, Changzheng | Jiang, Xiaoyi

Article Type: Research Article

Abstract: Clustering analysis is a powerful tool in customer segmentation. Although various algorithms have been proposed, the determination of the optimal number of clusters remains to be a difficult issue. In this paper, a clustering method based on consistency criterion is proposed to address this issue. The main characteristic of the new approach is that it requires little prior information and can find the optimal number of clusters automatically. Extensive comparisons are done over 22 real-world datasets from different domains, in which four well-known clustering algorithms in combination with six clustering indices are used as the benchmark methods. The results demonstrate …the superiority of our method in appropriately determining the number of clusters. An application of the new approach in customer segmentation of credit card users is also illustrated. Show more

Keywords: Customer segmentation, cluster analysis, number of clusters, consistency criterion

DOI: 10.3233/IDA-150727

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 471-484, 2015

Authors: Alizadeh, Hosein | Yousefnezhad, Muhammad | Bidgoli, Behrouz Minaei

Article Type: Research Article

Abstract: The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a novel feedback framework for the cluster ensemble problem, which we call Wisdom of Crowds Cluster Ensemble (WOCCE). Although many conventional cluster ensemble methods focusing on diversity have recently been proposed, WOCCE analyzes the conditions necessary for a crowd to exhibit this collective wisdom. These include decentralization …criteria for generating primary results, independence criteria for the base algorithms, and diversity criteria for the ensemble members. We suggest appropriate procedures for evaluating these measures, and propose a new measure to assess the diversity. We evaluate the performance of WOCCE against some other traditional base algorithms as well as state-of-the-art ensemble methods. The results demonstrate the efficiency of WOCCE's aggregate decision-making compared to other algorithms. Show more

Keywords: Cluster ensemble, Wisdom of Crowds, diversity, independency, K-means

DOI: 10.3233/IDA-150728

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 485-503, 2015

Authors: Zhao, Xi | Shi, Yong | Niu, Lingfeng

Article Type: Research Article

Abstract: Handling data classification and regression problems through linear hyperplane is a naive and simple idea. In this paper, inspired by the idea of multiple criteria linear programs (MCLP) and multiple criteria quadratic programs (MCQP), we proposed a novel method for binary classification and regression problem. There are two main advantages for the proposed approach. One is that both of these two models guarantee the existence of feasible solutions when the model parameters were chosen properly. The other is that nonlinear patterns could be handled and captured by introducing kernel function into MCLP framework with a more natural way than previous …work. Various classical approaches and datasets were evaluated in our experiments, and the result on both toy and real world data demonstrate the correctness and effectiveness of our proposed methods. Show more

Keywords: Multiple criteria linear program, optimization, binary classification, regression

DOI: 10.3233/IDA-150729

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 505-527, 2015

Authors: Luor, Dah-Chin

Article Type: Research Article

Abstract: In many real-world data sets, the range of values of each data attributes is quite different. Data standardization is a commonly used data preprocessing method which makes the values of data attributes fall within a defined specific range. A number of data standardization methods have been proposed in the literature and it is known that data standardization techniques can influence the performance of learning algorithms. Support vector machines (SVMs) are widely used kernel-based learning algorithms, and the basic idea is to transform data implicitly from the input space to the feature space, where a linear algorithm is used to …solve the classification problem. It is obvious that the process of data standardization will change the values of transformed data in the feature space. Therefore it is important to investigate the effect of standardization methods upon the performance of kernel-based classification algorithms. In this research a comparative assessment of data standardization methods is applied to find the effect on the performance of SVM learning algorithm for classification problems. Three simulated data sets and nine real-world data sets (with eight medical data sets) are employed to demonstrate the effect of nine different data standardization methods with two commonly used kernels, Gaussian and polynomial, on the performance of SVM. Accuracy index, type I error, type II error, and two measures, kernel target alignment and class separability measure, are the criteria to evaluate the effect of standardization methods. The experiment results show that a suitable standardization processing has significant improvement on the performance of SVM. On the other hand, a bad choice of standardization method will decrease the classification accuracy of SVM. Show more

Keywords: Data standardization, binary classification, support vector machine, kernels

DOI: 10.3233/IDA-150730

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 529-546, 2015

Authors: Lee, Chang-Hwan

Article Type: Research Article

Abstract: We present a new methodology for sequential classification, which employs sequential pattern generation and classification, in a two-stage process. In the first phase, a set of sequential patterns are generated from multi-dimensional sequence data. We proposes a novel method for inducing multi-dimensional sequential patterns with the use of Hellinger measure. The importance of each sequential pattern is also calculated. In the second phase, the generated sequential patterns are used for classifying multi-dimensional sequence data. A number of theorems are proposed to reduce the computational complexity of generating sequential patterns. The proposed method is tested on some synthesized sequence databases.

Keywords: Machine learning, sequential classification, sequential pattern

DOI: 10.3233/IDA-150731

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 547-561, 2015

Authors: Kamaei, Kamran | Altınçay, Hakan

Article Type: Research Article

Abstract: Two major drawbacks in nearest feature line classifier are the extrapolation and interpolation inaccuracies. The former can easily be counteracted by considering segments instead of lines. However, the solution of the latter problem is more challenging. Recently developed techniques tackle with this drawback by selecting a subset of the feature line segments either during training or testing. In this study, a novel scheme that is based on editing the feature line segments is proposed. It involves two major strategies, namely generalization-based elimination and representation-based elimination. In generalization-based elimination, the benefit and cost of discarding each feature line segment are compared …and the segments that contribute to the classification error are discarded. For the implementation of representation-based elimination, a measure of intersection is defined and the longer of each pair of intersecting segments are discarded. Moreover, the distances of the feature line segments to the samples of other classes are also investigated to discard inconsistent segments. The proposed steps are evaluated by applying them in different orders. Experimental results have shown that the proposed approach provides better accuracies when compared to two recently developed extensions of the nearest feature line approach, namely shortest feature line segment and rectified nearest feature line segment. Show more

Keywords: Nearest feature line, editing, intersecting segments, interpolation inaccuracy, extrapolation inaccuracy

DOI: 10.3233/IDA-150732

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 563-580, 2015

Authors: Castro, Nuno C. | Azevedo, Paulo J.

Article Type: Research Article

Abstract: The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX's parameters. The approach - AutoiSAX - not only discovers the …best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method. Show more

Keywords: Time series, data mining, representation, iSAX, parameters

DOI: 10.3233/IDA-150733

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 581-595, 2015

Authors: Fanaee-T, Hadi | Gama, João

Article Type: Research Article

Abstract: Syndromic surveillance systems continuously monitor multiple pre-diagnostic daily streams of indicators from different regions with the aim of early detection of disease outbreaks. The main objective of these systems is to detect outbreaks hours or days before the clinical and laboratory confirmation. The type of data that is being generated via these systems is usually multivariate and seasonal with spatial and temporal dimensions. The algorithm What's Strange About Recent Events (WSARE) is the state-of-the-art method for such problems. It exhaustively searches for contrast sets in the multivariate data and signals an alarm when find statistically significant rules. This bottom-up approach …presents a much lower detection delay comparing the existing top-down approaches. However, WSARE is very sensitive to the small-scale changes and subsequently comes with a relatively high rate of false alarms. We propose a new approach called EigenEvent that is neither fully top-down nor bottom-up. In this method, we instead of top-down or bottom-up search, track changes in data correlation structure via eigenspace techniques. This new methodology enables us to detect both overall changes (via eigenvalue) and dimension-level changes (via eigenvectors). Experimental results on hundred sets of benchmark data reveals that EigenEvent presents a better overall performance comparing state-of-the-art, in particular in terms of the false alarm rate. Show more

Keywords: Event detection, complex data streams, tensor decomposition, syndromic surveillance

DOI: 10.3233/IDA-150734

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 597-616, 2015

Authors: Valizadeh, Mohammadreza | Brazdil, Pavel

Article Type: Research Article

Abstract: Several algorithms based on PageRank algorithm have been proposed to rank the document sentences in the multi-document summarization field and LexRank and T-LexRank algorithms are well known examples. In literature different concepts such as weighted inter-cluster edge, cluster-sensitive graph model and document-sensitive graph model have been proposed to improve LexRank and T-LexRank algorithms (e.g. DsR-G, DsR-Q) for multi-document summarization. In this paper, a density-based graph model for multi-document summarization is proposed by adding the concept of density to LexRank and T-LexRank algorithms. The resulting generic multi-document summarization systems, DensGS and DensGSD were evaluated on DUC 2004 while the query-based variants, …DensQS, DensQSD were evaluated on DUC 2006, DUC 2007 and TAC 2010 task A. ROUGE measure was used in the evaluation. Experimental results show that density concept improves LexRank and T-LexRank algorithms and outperforms previous graph-based models (DsR-G and DsR-Q) in generic and query-based multi-document summarization tasks. Furthermore, the comparison of the number of iterations indicates that the density-based algorithm is faster than the other algorithms based on PageRank. Show more

Keywords: Unsupervised summarization, multi-document summarization, graph-based approach to summarization, performance of summarization, document density

DOI: 10.3233/IDA-150735

Citation: Intelligent Data Analysis, vol. 19, no. 3, pp. 617-629, 2015