Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

On the Application of Data Mining to Official Data

Retrieving valuable knowledge and statistical patterns from official data has a great potential in supporting strategic policy making. Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we provide an introduction to applications of DM to official statistics and flag the important issues and challenges. Considering recent advancements in software projects for DM, we propose intelligent data control system design and specifications as an example of DM application in official data processing.Data mining, Official data, Intelligent data control system

New rough set based maximum partitioning attribute algorithm for categorical data clustering

Clustering a set of data into homogeneous groups is a fundamental operation in data mining. Recently, consideration has been put on categorical data clustering, where the data set consists of non-numerical attributes. However, implementing several existing categorical clustering algorithms is challenging as some cannot handle uncertainty while others have stability issues. The Rough Set theory (RST) is a mathematical tool for dealing with categorical data and handling uncertainty. It is also used to identify cause-effect relationships in databases as a form of learning and data mining. Therefore, this study aims to address the issues of uncertainty and stability for categorical clustering, and it proposes an improved algorithm centred on RST. The proposed method employed the partitioning measure to calculate the information system's positive and boundary regions of attributes. Firstly, an attributes partitioning method called Positive Region-based Indiscernibility (PRI) was developed to address the uncertainty issue in attribute partitioning for categorical data. The PRI method requires the positive and boundary regions-based partitioning calculation method. Next, to address the computational complexity issue in the clustering process, a clustering attribute selection method called Maximum Mean Partitioning (MMP) is introduced by computing the mean. The MMP method selects the maximum degree of the mean attribute, and the attribute with the maximum mean partitioning value is chosen as the best clustering attribute. The integration of proposed PRI and MMP methods generated a new rough set hybrid clustering algorithm for categorical data clustering algorithm named Maximum Partitioning Attribute (MPA) algorithm. This hybrid algorithm is an all-inclusive solution for uncertainty, computational complexity, cluster purity, and higher accuracy in attribute partitioning and selecting a clustering attribute. The proposed MPA algorithm is compared against the baseline algorithms, namely Maximum Significance Attribute (MSA), Information-Theoretic Dependency Roughness (ITDR), Maximum Indiscernibility Attribute (MIA), and simple classical K-Mean. In addition, seven small data sets from previously utilized research cases and 21 UCI repository and benchmark datasets are used for validation. Finally, the results were presented in tabular and graphical form, showing the proposed MPA algorithm outperforms the baseline algorithms for all data sets. Furthermore, the results showed that the proposed MPA algorithm improves the rough accuracy against MSA, ITDR, and MIA by 54.42%. Hence, the MPA algorithm has reduced the computational complexity compared to MSA, ITDR, and MIA with 77.11% less time and 58.66% minimum iterations. Similarly, a significant percentage improvement, up to 97.35%, was observed for overall purity by the MPA algorithm against MSA, ITDR, and MIA. In addition, the increment up to 34.41% of the overall accuracy of simple K-means by MPA has been obtained. Hence, it is proven that the proposed MPA has given promising solutions to address the categorical data clustering problem

Developing reliable anomaly detection system for critical hosts: a proactive defense paradigm

Current host-based anomaly detection systems have limited accuracy and incur high processing costs. This is due to the need for processing massive audit data of the critical host(s) while detecting complex zero-day attacks which can leave minor, stealthy and dispersed artefacts. In this research study, this observation is validated using existing datasets and state-of-the-art algorithms related to the construction of the features of a host's audit data, such as the popular semantic-based extraction and decision engines, including Support Vector Machines, Extreme Learning Machines and Hidden Markov Models. There is a challenging trade-off between achieving accuracy with a minimum processing cost and processing massive amounts of audit data that can include complex attacks. Also, there is a lack of a realistic experimental dataset that reflects the normal and abnormal activities of current real-world computers. This thesis investigates the development of new methodologies for host-based anomaly detection systems with the specific aims of improving accuracy at a minimum processing cost while considering challenges such as complex attacks which, in some cases, can only be visible via a quantified computing resource, for example, the execution times of programs, the processing of massive amounts of audit data, the unavailability of a realistic experimental dataset and the automatic minimization of the false positive rate while dealing with the dynamics of normal activities. This study provides three original and significant contributions to this field of research which represent a marked advance in its body of knowledge. The first major contribution is the generation and release of a realistic intrusion detection systems dataset as well as the development of a metric based on fuzzy qualitative modeling for embedding the possible quality of realism in a dataset's design process and assessing this quality in existing or future datasets. The second key contribution is constructing and evaluating the hidden host features to identify the trivial differences between the normal and abnormal artefacts of hosts' activities at a minimum processing cost. Linux-centric features include the frequencies and ranges, frequency-domain representations and Gaussian interpretations of system call identifiers with execution times while, for Windows, a count of the distinct core Dynamic Linked Library calls is identified as a hidden host feature. The final key contribution is the development of two new anomaly-based statistical decision engines for capitalizing on the potential of some of the suggested hidden features and reliably detecting anomalies. The first engine, which has a forensic module, is based on stochastic theories including Hierarchical hidden Markov models and the second is modeled using Gaussian Mixture Modeling and Correntropy. The results demonstrate that the proposed host features and engines are competent for meeting the identified challenges