Data Mining - Concepts and Techniques.

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

Article history: Received June25, 2015 Received in revised format: September 22, 2015 Accepted September 24, 2015 Available online September 25 2015 The teaching-learning-based optimization (TLBO) algorithm is finding a large number of applications in different fields of engineering and science since its introduction in 2011. The major applications are found in electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics, chemistry, biotechnology and economics. This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems. The tutorial is expected to be useful to the beginners.

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Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

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Ad Hoc Networks: Technologies and Protocols

This survey gives state-of-the-art of genetic algorithm (GA) based clustering techniques. Clustering is a fundamental and widely applied method in understanding and exploring a data set. Interest in clustering has increased recently due to the emergence of several new areas of applications including data mining, bioinformatics, web use data analysis, image analysis etc. To enhance the performance of clustering algorithms, Genetic Algorithms (GAs) is applied to the clustering algorithm. GAs are the best-known evolutionary techniques. The capability of GAs is applied to evolve the proper number of clusters and to provide appropriate clustering. This paper present some existing GA based clustering algorithms and their application to different problems and domains.

Genetic Algorithm Based Clustering: A Survey

A mobile ad hoc network (MANET) is a self-organized wireless short-lived network consisting of mobile nodes. The mobile nodes communicate with one another by wireless radio links without the use of any pre-established fixed communication network infrastructure. The mobile nodes are vulnerable to different types of security attacks that allow interception, injection, and interference of communication among nodes. Possible damages include leaking secret information, message contamination and node impersonation. MANETs need secure routing protocols to prevent possible security attacks. In this paper, we evaluate the performance of a new security protocol against various known and unknown malicious node attacks. Simulation results have shown that the proposed security protocol resists against malicious nodes with low implementation complexity.

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Impact of Security Attacks on a New Security Protocol for Mobile Ad Hoc Networks

The k-means algorithm is well-known for its efficiency in clustering large data sets and it is restricted to the numerical data types. But the real world is a mixture of various data typed objects. In this paper we implemented algorithms which extend the k-means algorithm to categorical domains by using Modified k-modes algorithm and domains with mixed categorical and numerical values by using k-prototypes algorithm. The Modified k-modes algorithm will replace the means with the modes of the clusters by following three measures like “using a simple matching dissimilarity measure for categorical data”, “replacing means of clusters by modes” and “using a frequency-based method to find the modes of a problem used by the k-means algorithm”. The other algorithm used in this paper is the k-prototypes algorithm which is implemented by integrating the Incremental k-means and the Modified k-modes partition clustering algorithms. All these algorithms reduce the cost function value.

Cluster Analysis on Different Data Sets Using K-Modes and K-Prototype Algorithms

This paper presents a new data classification method based on particle swarm optimization (PSO) techniques. The paper discusses the building of a classifier model based on multiple regression linear approach. The coefficients of multiple regression linear models (MRLMs) are estimated using least square estimation technique and PSO techniques for percentage of correct classification performance comparisons. The mathematical models are developed for many real world datasets collected from UCI machine repository. The mathematical models give the user an insight into how the attributes are interrelated to predict the class membership. The proposed approach is illustrated on many real data sets for classification purposes. The comparison results on the illustrative examples show that the PSO based approach is superior to traditional least square approach in classifying multi-class data sets.

Particle swarm optimized multiple regression linear model for data classification

In the present scenario of global economy and World Wide Web, large sets of evolving and distributed data can be handled efficiently by incremental data mining. Frequent patterns are very important in knowledge discovery and data mining process, such as mining of association rules, correlations. FP-tree is a very versatile data structure used for mining of frequent patterns in knowledge discovery and data mining process. FP-tree is a compact representation of transaction database that contains frequency information of all relevant frequent patterns (FP) of the database. All of the existing incremental frequent pattern mining algorithms, such as AFPIM, CATS, CanTree, CP-tree, and SPO-tree, perform incremental mining by processing one transaction of the incremental part of database at a time and updating it to the FP-tree of initial (original) database. Here, in this paper, we propose a novel method that takes advantage of FP-tree representation of incremental transaction database for incremental mining. We propose a batch incremental processing algorithm BIT_FPGrowth that restructures and merges two small consecutive duration FP-trees to obtain a FP-tree of the FP-Growth algorithm. Our BIT_FPGrowth uses FP-tree as preprocessed data repository to get transactions (i.e., item-sets), unlike other sequential incremental algorithms that read transactions from database. BIT_FPGrowth algorithm takes less time for constructing FP-tree. Our experimental results show that, as the size of the database increases, increase in runtime of BIT_FPGrowth is much less and is least of all the other algorithms.

Batch incremental processing for FP-tree construction using FP-Growth algorithm

Clustering is a process of putting similar data into groups. This paper presents data clustering using improved genetic algorithm (IGA) in which an efficient method of crossover and mutation are implemented. Further it is hybridized with the popular NelderMead (NM) Simplex search and K-means to exploit the potentiality of both in the hybridized algorithm. The performance of hybrid approach is evaluated with few data clustering problems. Further a Variable Length IGA is proposed which optimally finds the clusters of benchmark image datasets and the performance is compared with K-means and GCUK[12].The results revealed are very encouraging with IGA and its hybridization with other algorithms.

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P. V. G. D. Prasad Reddy

Papers

Impact of Security Attacks on a New Security Protocol for Mobile Ad Hoc Networks

Cluster Analysis on Different Data Sets Using K-Modes and K-Prototype Algorithms

Particle swarm optimized multiple regression linear model for data classification

Batch incremental processing for FP-tree construction using FP-Growth algorithm

Hybridized Improved Genetic Algorithm with Variable Length Chromosome for Image Clustering