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The purpose of this paper is to present a novel control mechanism for avoiding overlapping among biclusters in expression data.

Biclustering is a technique used in analysis of microarray data. One of the most popular biclustering algorithms is introduced by Cheng and Church (2000) (Ch&Ch). Even if this heuristic is successful at finding interesting biclusters, it presents several drawbacks. The main shortcoming is that it introduces random values in the expression matrix to control the overlapping. The overlapping control method presented in this paper is based on a matrix of weights, that is used to estimate the overlapping of a bicluster with already found ones. In this way, the algorithm is always working on real data and so the biclusters it discovers contain only original data.

The paper shows that the original algorithm wrongly estimates the quality of the biclusters after some iterations, due to random values that it introduces. The empirical results show that the proposed approach is effective in order to improve the heuristic. It is also important to highlight that many interesting biclusters found by using our approach would have not been obtained using the original algorithm.

The original algorithm proposed by Ch&Ch is one of the most successful algorithms for discovering biclusters in microarray data. However, it presents some limitations, the most relevant being the substitution phase adopted in order to avoid overlapping among biclusters. The modified version of the algorithm proposed in this paper improves the original one, as proven in the experimentation.

For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.

In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.

To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.

The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information.

To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix.

The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score.

The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products.

This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

Semantic technologies are a potential approach to enhancing the use of the web of knowledge. An experiment was conducted to investigate the roles of two semantic techniques, namely concept recommendation and mind maps, for females and males This paper aims to document this research.

The study compared the searching behaviours and perceptions of searching strategies in the search environment with techniques to reveal the gender difference in the use of semantic technologies. The study further investigated how the techniques influenced female and male students' searching experiences by comparing their searching behaviours and strategies in two different environments, one developed with the semantic technologies and one without.

Although the techniques were helpful for both female and male students in improving their perceptions of searching strategies, there were gender differences in sense of disorientation, problem solving and searching patterns. In particular, the techniques facilitated females to search for information beyond similarity holes, but did not demonstrate such an effect on males. However, they supported males to organize information for better use.

The study is only a small‐scale investigation. Further studies need to be conducted with a larger sample to obtain robust evidence.

In this paper, the effects of semantic technologies are evaluated from the perspectives of education and the human factor, rather than only in terms of technical performance.

Blockmodeling is viewed often as a data reduction method. However, this is a simplistic view of the class of methods designed to uncover social structures, identify subgroups, and reveal emergent roles. Worse, this view misses the richness of the method as a tool for uncovering novel human resource management (HRM) insights. Here, we provide a brief overview of some essentials of blockmodeling and discuss research questions that can be addressed using this approach in applied HRM settings. Finally, we offer an empirical example to illustrate blockmodeling and the types of information that can be gleaned from its implementation.

The purpose of this paper is to present a novel approach for niche-market tour identification, with the objective to obtain a better segmentation of target tourists and support the design of tourism products. A proposed system, namely the Niche Tourism Identification System (NTIS) was implemented based on the proposed scheme and its functionality was showcased in a case study undertaken with a local travel agency.

The proposed system implements automated customer market segmentation, based on similar characteristics that can be collected from potential customers. After that, special-interest tourism-based market strategies and products can be designed for the potential customers. The market segmentation is conducted using a GA-based k-means clustering engine (GACE), while the parameter setting is controlled by the travel agents.

The proposed NTIS was deployed in a real-world case study which helps a local travel agency to determine the various types of niche tourism found in the existing market in Hong Kong. Its output was reviewed by experience tour planners. It was found that with the niche characteristics can be successfully revealed by summarizing the possible factors within the potential clusters in the existing database. The system performed consistently compared to human planners.

To the best of the authors’ knowledge, although some alternative methods for segmenting travel markets have been proposed, few have provided any effective approaches for identifying existing niche markets to support online inquiry. Also, GACE has been proposed to compensate for the limitations that challenge k-means clustering in binding to a local optimum and for its weakness in dealing with multi-dimensional space.

The purpose of this paper is to apply a multi‐objective Bayesian artificial immune system (MOBAIS) to feature selection in classification problems aiming at minimizing both the classification error and cardinality of the subset of features. The algorithm is able to perform a multimodal search maintaining population diversity and controlling automatically the population size according to the problem. In addition, it is capable of identifying and preserving building blocks (partial components of the whole solution) effectively.

The algorithm evolves candidate subsets of features by replacing the traditional mutation operator in immune‐inspired algorithms with a probabilistic model which represents the probability distribution of the promising solutions found so far. Then, the probabilistic model is used to generate new individuals. A Bayesian network is adopted as the probabilistic model due to its capability of capturing expressive interactions among the variables of the problem. In order to evaluate the proposal, it was applied to ten datasets and the results compared with those generated by state‐of‐the‐art algorithms.

The experiments demonstrate the effectiveness of the multi‐objective approach to feature selection. The algorithm found parsimonious subsets of features and the classifiers produced a significant improvement in the accuracy. In addition, the maintenance of building blocks avoids the disruption of partial solutions, leading to a quick convergence.

The originality of this paper relies on the proposal of a novel algorithm to multi‐objective feature selection.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

Today clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by effective personalized recommendation. The purpose of this paper is to propose a personalized recommendation model and algorithm based on Kansei engineering, traditional filtering algorithm and the knowledge relating to apparel.

Users’ perceptual image and the design elements of apparel based on Kansei engineering are discussed to build the mapping relation between the design elements and user ratings employing verbal protocol, semantic differential and partial least squares. The implicit knowledge and emotional needs pertaining to users are accessed using analytic hierarchy process. A personalized recommendation model for apparel online shopping is established and the algorithm for the personalized recommendation process is proposed. To present the personalized recommendation model, men’s plaid shirts are taken as the example, and the recommendations of apparel for online shopping were implemented and ranked in the context of differing users’ emotional needs. A comparison between the traditional model and this model is made to verify the effectiveness.

The recommendation model is capable of analyzing data and information effectively, and providing fast, personalized apparel recommendation services in accordance with users’ emotional needs. The experimental results suggest that the model is effective.

Similar researches of recommendation mainly focus on the field of computer science, the basic idea of which is using users’ history accessing records or the preferences of other similar users for determination of users’ preferences. Since the attributes of apparel products are not factored in the approach referred above, the issue of personalized recommendation cannot be solved in a really effective way. Combining Kansei engineering and recommendation algorithm, a framework for apparel product recommendation is presented and it is a new way for improvement of recommendations for apparel products on shopping sites.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.