A NOVEL APPROACH: VARIOUS CLASSIFICATIONS IN WEB MINING USING DIAGNOSIS OF FAULTS IN ELECTRO- MECHANICAL DEVICES FROM VIBRATION MEASUREMENTS

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 10, October 2017, pp , Article ID: IJMET_08_10_009 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed A NOVEL APPROACH: VARIOUS CLASSIFICATIONS IN WEB MINING USING DIAGNOSIS OF FAULTS IN ELECTRO- MECHANICAL DEVICES FROM VIBRATION MEASUREMENTS A. Sangeetha PhD Scholar, Department of Computer Science & Engineering, Bharath University, Chennai, India Dr. C. Nalini Professor, Department of Computer Science & Engineering, Bharath University, Chennai, India ABSTRACT Nowadays web mining is getting significant in the domain of data mining due to the frequent applications obtained for the society. However the research community finds the difficulty in tackling web mining. It is familiar and most interested concepts in web mining. It is the application of data mining techniques to discover patterns from the World Wide Web. It is discovering useful information from the World-Wide Web and its usage patterns. The objective of this web mining several classifications methods to discover an efficient and most effective performance of new pattern in web mining. Key words: Jrip, BayesNet, Web content mining, Misc, Web usage mining, Dagging, Web structure mining. Cite this Article: A. Sangeetha and Dr. C. Nalini, A Novel Approach: Various Classifications in Web Mining using Diagnosis of Faults in Electro-Mechanical Devices from Vibration Measurements, International Journal of Mechanical Engineering and Technology 8(10), 2017, pp INTRODUCTION Web mining is the application of data mining techniques to extract knowledge from web data, including web documents, hyperlinks between documents, usage logs of web sites, etc. A panel organized at ICTAI 1997 (Srivastava and Mobasher 1997) asked the question Is there anything distinct about web mining? While no definitive conclusions were reached then, the tremendous attention on web mining in the past decades, and a number of significant ideas that have been created, have certainly answered this question in the assenting in a big way. A 65 editor@iaeme.com

2 A. Sangeetha and Dr. C. Nalini fairly stable community of researchers interested in the area has been formed, largely through the successful series of Web KDD workshops, It have been held annually in conjunction with the ACM SIGKDD Conference since 1999 (Masand and Spiliopoulou 1999; Kohavi, Spiliopoulou, and Srivastava 2001; Kohavi, Masand, Spiliopoulou, and Srivastava 2001; Masand, Spiliopoulou, Srivastava, and Zaiane 2002), the web analytics workshops, It have been held in conjunction with the SIAM data mining conference (Ghosh and Srivastava 2001a, b). A good survey of the research in the field (through 1999) is provided by Kosala and Blockeel (2000) and Madria, Bhowmick, Ng, and Lim (1999). Two different approaches were taken in initially defining web mining. First was a process-centric view, which defined web mining as a sequence of tasks (Etzioni 1996). Second was a data-centric view, it has defined web mining in terms of the types of web data that was being used in the mining process (Cooley, Srivastava, and Mobasher 1997). The second definition has become more acceptable, as is evident from the approach adopted in most recent papers (Madria, Bhowmick, Ng, and Lim 1999; Borges and Levene 1998; Kosala and Blockeel 2000) that have addressed the issue. The related works around web mining in order to link the relevance of our work section II describes the terms and methods in order to enhance the readability of the research work. In the section III presents the main experiments applied for main approach and the results with analysis are presented in the section IV, Finally Section V presents the conclusion and future works of the proposed methods. 2. TERMS AND METHODS In this section presents the terminology and methods to be used in this research work. Web Usage Mining: It is the application of data mining techniques to discover usage patterns from web usage data. Usage data captures the identity or origin of web users along with their browsing behavior at a web site. Web Content Mining: It is the process of extracting useful information from the contents of web documents. It has text, images, audio, video and structured records (i.e., List and Tables). Web Structure Mining: It is the process of discovering structure information from the web. The structure of a typical web graph consists of web pages as nodes, and hyperlinks as edges connecting related pages. BayesNet: Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier. It Provides datastructures and facilities common to Bayes Network learning algorithms like K2 and B. Dagging: This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. JRip: This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER) J48: Class for generating a pruned or unpruned C4.5 decision tree editor@iaeme.com

3 A Novel Approach: Various Classifications in Web Mining using Diagnosis of Faults in Electro- Mechanical Devices from Vibration Measurements Figure 1 Ontology of Web Mining HyperPipes: For each category a HyperPipe is constructed that contains all points of that category. Test instances are classified according to the category that "most contains the instance". There are two data analysis approaches in web mining namely Machine Learning Approach Data Mining Approach. The Machine Learning emphasizes sophisticated algorithms and the data sets tend to be small, fit in memory. The Data Mining Approach data cannot even fit on a single disk and it leads to simpler algorithms. Approaches of Data Analysis Machine Learning Approaches Data Mining Approaches Figure 2 Approaches of Data Analysis in web mining 3. EXPERIMENTAL SETUP In this experiment the dataset is downloaded from The content of this data includes eight attributes namely, dummy, class, #, sup, cpm, mis, misr, dir, omega and six base classes with four derived classes. It contains Categorical, Integer and Real attributes and also two 67 editor@iaeme.com

4 A. Sangeetha and Dr. C. Nalini hundred and nine instances. The records are tabulated in the format in the table Table1&Table 2. Table 1 Description of Mechanical Analysis Dataset No Tag Attribute Name and Meaning of Attribute 0 dummy (always 1) used for numbering - ignore 1 class classification (1..6, the same for components of one example) 2 # component number (integer) 3 sup support in the machine where measure was taken (1..4) 4 cpm frequency of the measure (integer) 5 mis measure (real) 6 misr earlier measure (real) 7 dir filter, type of the measure and direction 8 omega rpm of the machine (integer, the same for components of one example) Table 2 Class names of Mechanical Analysis Dataset S.No Class Type Name of the class 1 Base Class Problems in the joint 2 Base Class Faulty Bearings 3 Base Class Mechanical Loosening 4 Base Class Basement Distortion 5 Base Class Unbalance 6 Base Class Normal operating conditions 7 Derived Class (includes class 1 and class 4) Shaft misalignment 8 Derived Class(includes classes 2, 3 and 5) Problems in the pump 9 Derived Class(includes classes 2, 3 and 5) Problems in the motor 10 Derived Class(includes all basic classes except class 6) Problems in the machine 4. RESULT AND ANALYSIS In this experimental exercise we select four categories of classifications namely Bayes type, Ensemble (Meta) type, Decision Tree type and Decision rule type and miscellaneous type. Figure 3 Architecture of Model Evaluation for Mechanical Analysis Dataset 68 editor@iaeme.com

5 A Novel Approach: Various Classifications in Web Mining using Diagnosis of Faults in Electro- Mechanical Devices from Vibration Measurements Several methods BayesNet from Bayes classification, Dagging from Meta classification, J48 from Trees classification and JRip from Rules classification and HyperPipes from Misc. Table 3 Performance of Classifiers S.No Name of the Classifiers Category of the Accuracy 1 BayesNet Classifiers Bayes 84.98% 2 Dagging Meta 88.35% 3 JRip Rules 87.58% 4 J48 Trees 66.76% 5 HyperPipes Misc 78.02% The above table represents the proposed models Dagging from Meta classification is highest accuracy compare than other classifiers but BayesNet and JRip are also nearest to the Dagging in ensemble classifiers. Figure 4 Comparison of various classifiers The above graph is clearly shown Dagging has 88.35% accuracy level in ensemble model classifier model. It dominates other classifier models and J48 model has lowest accuracy level 66.76%. It is very lowest accuracy compare than other classifier models. BayesNet, Dagging and JRip have accuracy level above 80%. HyperPipes has 78.02% accuracy level. Dagging leads the highest level accuracy and it dominates all classifier models. 5. CONCLUSIONS AND FUTURE WORKS Our novel approach based on the various classifiers yield better results as demonstrated by our experiments. Our proposed method Dagging is recommended to the extension of future works. This model is contributed to the Mechanical and computer research society editor@iaeme.com

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