Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.

/pdf/data-stream-clustering-a-survey-22l53djrxp.pdf

Data stream clustering: A survey

Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for tumor data in gene expression becomes a key point for tumor classification. Inspired by the concept of gain ratio in decision tree theory, an attribute selection method based on fuzzy gain ratio under the framework of fuzzy rough set theory is proposed. The approach is compared to several other approaches on three real world tumor data sets in gene expression. Results show that the proposed method is effective. This work may supply an optional strategy for dealing with tumor data in gene expression or other applications.

Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification

Organizations are under increasing pressure to apply digital technologies to renew and transform their business models. A great deal of research has examined specific phenomena, such as adoption antecedents and design methods. However, it is unclear what we know in totality, including what research streams exist, how they fit together, and fruitful opportunities for new knowledge development. We combine scientometric and systematic literature review methodologies to examine 7 dimensions of an adapted theoretical framework: initiation; development; implementation; exploitation; the role of the external competitive environment; role of internal organizational environment; and product, service, and process outcomes. From a macro perspective, we find vastly uneven coverage of research streams, diversity and diffusiveness of research, and knowledge and learning as an underlying conceptual pillar. Combined with our summary of each of the 7 research streams, these findings suggest several areas of future research, which we develop by identifying oppositions and tensions.

/pdf/digital-innovation-a-review-and-synthesis-48a5kfm0lx.pdf

Digital innovation: A review and synthesis

Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. While the efficiency of a corporation's management is generally acknowledged to be a key contributor to corporation's bankrupt, it is usually excluded from early prediction models. The objective of the study is to use efficiency as predictive variables with a proposed novel model to integrate rough set theory (RST) with support vector machines (SVM) technique to increase the accuracy of the prediction of business failure. In the proposed method (RST-SVM), data envelopment analysis (DEA) is employed as a tool to evaluate the input/output efficiency. Furthermore, by RST approach, the redundant attributes in multi-attribute information table can be reduced, which showed that the number of independent variables was reduced with no information loss, is utilized as a preprocessor to improve business failure prediction capability by SVM. The effectiveness of the methodology was verified by experiments comparing back-propagation neural networks (BPN) approach with the hybrid approach (RST-BPN). The results shows that DEA do provide valuable information in business failure predictions and the proposed RST-SVM model provides better classification results than RST-BPN model, no matter when only considering financial ratios or the model including both financial ratios and DEA.

A hybrid approach of DEA, rough set and support vector machines for business failure prediction

A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the reduct set of the set of all attributes of the decision system. The reduct set is used as preprocessing technique for classification of the decision system in order to bring out the potential patterns or association rules or knowledge through data mining techniques. Several researchers have contributed variety of algorithms for computing the reduct sets by considering different cases like inconsistency, missing attribute values and multiple decision attributes of the decision system. This paper focuses on the review of the techniques for dimensionality reduction under rough set theory environment. Further, the rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have also been reviewed. The performance analysis of the algorithms has been discussed in connection with the classification.

Review: Dimensionality reduction based on rough set theory: A review

There is a rapid growth in internet and website usage. A wide variety of devices are used to access websites, such as mobile phones, tablets, laptops, and personal computers. Attackers are finding more and more vulnerabilities on websites that they can exploit for malicious purposes. A web application attack occurs when cyber criminals gain access to unauthorized areas. Typically, attackers look for vulnerabilities in web applications at the application layer. SQL injection attacks and Cross Site script attacks is used to access web applications to obtain sensitive data. A key objective of this work is to develop new features and investigate how automatic tuning of machine learning techniques can improve the performance of Web Attack detections that use HTTP CSIC datasets to block and detect attacks. The Stepwise Conditional parameter tuning in machine learning algorithms is a proposed model. This model is a dynamic and automated parameter choosing and tuning based on the better outcome. This work also compares two datasets for performance of the proposed model.

/pdf/web-attack-prediction-using-stepwise-conditional-parameter-2f3scdo6.pdf

A. Pethalakshmi

Papers

Review: Dimensionality reduction based on rough set theory: A review

Web Attack Prediction using Stepwise Conditional Parameter Tuning in Machine Learning Algorithms with Usage Data