ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING MARCH 2015 VOL-5 NO-3
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.5 No.3 March 2015
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015
From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. New Algorithm Lets Robots Autonomously Plan for Tasks have developed a new algorithm that lets autonomous robots divvy up assembly tasks on the fly, an important step forward in multirobot cooperation. Today’s industrial robots are remarkably efficient as long as they are in a controlled environment where everything is exactly where they expect. But put them in an unfamiliar setting, where they have to think for themselves, and their efficiency plummets. And the difficulty of on-the-fly motion planning increases exponentially with the number of robots involved. For even a simple collaborative task, a team of, say, three autonomous robots might have to think for several hours to come up with a plan of attack. A new algorithm that can significantly reduce robot teams’ planning time. The plan the algorithm produces may not be perfectly efficient, but in many cases, the savings in planning time will more than offset the added execution time. Data mining the extraction of hidden predictive information from large databases is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Inkjet-Printing System Could Enable Mass-Production of Large-Screen OLED Displays based on years has developed It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.
Thanks, Editorial Team IJITCE
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015
Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.
Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015 Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015 Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015 Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015
Contents A Domain Ontology Method for Semantic Conceptual Distance based on Rule Ranking Algorithm S.Antoinette Aroul Jeyanthi & Dr.S.Pannirselvam ……………………………….……………………….[265]
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015
A Domain Ontology Method for Semantic Conceptual Distance based on Rule Ranking Algorithm S.Antoinette Aroul Jeyanthi Ph.D (Research Scholar), Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Email: jayanthijames@yahoo.com Dr.S.Pannirselvam Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India. Email: pannirselvam08@gmail.com Abstract— The problem of finding interesting and actionable patterns is a major challenge in data mining. It has been studied by many data mining researchers. The issue is that data mining algorithms often generate too many patterns, which make it very hard for the user to find those truly useful ones. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. In this paper, proposed and implemented an approach for ranking the rules using the semantic conceptual distance based on domain ontology which is represented as DAG. Keywords— Domain ontology, Unexpectedness, Association rule, Interestingness, Conceptual distance.
1. INTRODUCTION Knowledge discovery in data mining has been defined in Fayyad et al., [1] as the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns from data. Association rule algorithms Agrawal et al., [2] are rule-discovery methods that discover patterns in the form of IF-THEN rules. It has been noticed that most of the algorithms that perform data mining generate a large number of rules that are valid but obvious or not interesting to the user. To address this issue, most of the approaches to knowledge discovery use objective measures of interestingness for the evaluation of the discovered rules, such as confidence and support measures. These approaches capture the statistical strength of a pattern. The interestingness of a rule is essentially subjective. Subjective measures of interestingness, such as unexpectedness. Assume that the interestingness of a pattern depends on the decision-maker and does not solely depend on the statistical strength of the pattern. Although objective measures are useful, they are insufficient in the determination of the interestingness of the rules. One way to address this problem is by focusing on discovering unexpected patterns, where the unexpectedness of the discovered patterns is usually defined relative to a system of prior expectations. Moreover, ontology represents knowledge. Ontology is organized as a DAG (Directed Acyclic Graph) hierarchy. Ontologies allow domain knowledge to be represented explicitly and formally in such a way that it can be shared among human and computer systems. In this paper, we propose a new approach that adds intelligence and autonomy for ranking rules according to their conceptual distance (the distance between the antecedent and the consequent of the
rule) relative to the hierarchy. In other words, highly related concepts are grouped together in the hierarchy. The more distant the concepts are, the less they are related to each other. For concepts that are part of the definition of a rule, the less the concepts are related to each other, the more the rule is surprising and therefore interesting. With such a ranking method, a user can check fewer rules on the top of the list to extract the most pertinent ones. 2. LITERATURE SURVEY The unexpectedness of patterns has been studied in defined in comparison with user beliefs. A rule is considered to be interesting if it affects the levels of conviction of the user. The unexpectedness is defined as a distance, and it is based on a syntactic comparison between a rule and a conviction. Padmanabhan [3] et al., focused is on discovering minimal unexpected patterns rather than using any of the postprocessing approaches, such as filtering, to determine the minimal unexpected patterns from the set of all of the discovered patterns. Liu [4] et al., developed a subjective interestingness (unexpectedness) of a discovered pattern is characterized by asking the user to specify a set of patterns according to his/her previous knowledge or intuitive feelings. This specified set of patterns is then used by a fuzzy matching algorithm to match and rank the discovered patterns. Sahar [5] studied a genetic algorithm to dynamically maintain and search populations of rule sets for the most interesting rules rather than act as a post-processor. McGarry [6] identified by the genetic algorithm compared favorably with the rules selected by the domain expert. To find subjectively interesting rules, most existing approaches ask the user to explicitly specify what types of rules are interesting and uninteresting, then generate or retrieve those matching rules. This research on the unexpectedness makes a syntactic or semantic comparison between a rule and a belief. 3. METHODOLOGY 3.1 RULE INTERESTINGNESS MEASURES In this research work in data mining has shown that the interestingness of a rule can be measured using objective measures and subjective measures. Objective measures
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015 involve analyzing the rule’s structure, predictive performance, and statistical significance. In association to rule mining, such measures include support and confidence. However, it is noted in that such objective measures are insufficient for determining the interestingness of a discovered rule. Indeed, subjective measures are needed. There are two main subjective interestingness measures, namely unexpectedness and action ability. Unexpectedness: Rules are interesting if they are unknown to the user or contradict the user’s existing knowledge. Actionability: Rules are interesting if the user can do something with them to his/her advantage. 3.2 CONCEPTUAL DISTANCE Two main categories of algorithms for computing the semantic distance between terms organized in a hierarchical structure have been proposed in the literature: distance-based approaches and information content-based approaches. The general idea behind the distance-based algorithms is to find the shortest path between two concepts in terms of the number of edges. The shorter the path from one node to the other, the more similar they are. The problem with this approach is that it relies on the notion that edges in taxonomy represent uniform distances. Information content-based approaches are inspired by the perception that pairs of concepts that share many common contexts are semantically related. The more information that two concepts share in common, the more similar. The problem of the ontology distance is that it is highly dependent on the construction of the ontology. The measure is, highly dependent on oftentimes subjective ontology engineering decisions. To address this problem, we are associating a weight to any concept in the ontology that represents the degree of importance of this concept in the ontology along with the strength of any relation between the concepts. In an IS-A semantic network, the simplest form of determining the distance between two concept nodes, A and B, is the shortest path that links A and B. The minimum number of edges that separate A and B or the sum of the weights of the arcs along the shortest path between A and B.
Association Rules
Domain Ontology
Ranking Algorithm
Ranked Rules
Fig.1 Process flow A. Concept Semantic Distance The semantic distance between the two concepts A and B is the sum of the weights of the arcs along the shortest path between A and B. To compute the shortest path between two nodes using Dijkstra’s algorithm. To compute the distance between groups of concepts, for a given rule R: X → Y, where X = X1 … Xk, Y = Y1 … Ym, using the Hausdorff distance. The function h(X,Y) is called the directed Hausdorff distance from X to Y. This expression measures the conceptual distance between groups X = X1 … Xk and Y = Y1 … Ym of the concepts that contain the k Xi and m atomic Yj concepts, respectively. B. Rule Ranking Algorithm In this section, introduce an algorithm to rank the rules according to their conceptual distance based on a domain ontology that represents the background knowledge. The rules that we consider are in the form of “body → head”, where “body” and “head” are conjunctions of concepts in the vocabulary of the ontology. Here assume that other techniques carry out the task of pattern discovery and eliminate the patterns that do not satisfy the objective criteria. With such a ranking, a user can check only confirm the rules that are the most pertinent.
∧ ∧
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3.4 PROPOSED ALGORITHM The entire procedure is presented as simple algorithms. 3.4.1 Algorithm – I
3.3 PROPOSED METHODOLOGY The feature extraction is an important process to make efficient rule ranking algorithm. Hence an enhanced technique is used to extract the feature. 3.3.1 ONTOLOGY BASED RULE RANKING ALGORITHM The technique analyzes rules and detects the interrelation between various diseases and symptoms which are not directly associated in the dataset. The weight was set to 1 for all the symptoms that are directly associated to diseases. For the indirect symptoms the weight was increased by 1 at each level. Then the algorithm computes their semantic conceptual distance. The larger the distance is, the more the rule is interesting.
ND: Number of nodes R: Set of rules R = {Ri/Ri = body → head} where i ∊ [1,N] N: number of rules D: Maximum depth of the hierarchy Xi, Yj: Atomic Concepts; i ∊ [1,k]; j ∊ [1,m] Body = X1 … Xk Head = Y1 … Ym for i = 1 to ND for j = 1 to ND Begin // ShortestPath(Xi,Xj) shortest path between Xi and Xj// //Make a call to the weight(ShortestPath(Xi,Xj) above// Distance(Xi,Xj) = weight(ShortestPath(Xi,Xj)); End for i = 1 to N Distance(Ri) = (Distance(X1 … Xk; Y1 … Ym)); Sort Distance(Ri) descending;
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.3 MARCH 2015 4. EXPERIMENTATION & RESULTS In this experiment, the specific set of discovered rules is considered as input for the algorithm. The dynamic formation of the associations and interconnection between diseases and respective symptoms are implemented. Using dynamic formation of the graph data structure, the distance based on the depth based search is possible. At the initial level, the distance from the graph data structure of association and dependent rules is evaluated for all the instances. The following set of rules are considered as input for our experiment Rule1: headache, cold, cough -> fever Rule2: hairloss, weightloss, swallow-> cancer Rule3: sneeze, cough, cold-> viral Rule4: cough, fever, fatique->flu Rule5: fever, hairloss, fatique-> cancer The domain ontology which is represented as DAG of the given set of rules is as follows
From the results, the rules with the highest distance are considered as unexpected rules and are given the highest rank. The symptoms associated with that rule do not have direct association. They are indirectly associated with each other. The decision maker has to give more attention to the rare combination of symptoms than the common, directly associated symptoms. In the other ranking algorithms, the directly associated symptoms have the highest rank. 7. CONCLUSION In this paper, proposed a new approach for ranking association rules according to their conceptual distance, which was defined on the basis of the ontological distance. The proposed ranking algorithm helps the user to identify interesting association rules, particularly indirectly associated and unexpected rules. This algorithm uses domain ontology to calculate the distance between the antecedent and the consequent of the rules on which the ranking is based. The larger the conceptual distance is, the more the rule represents a high degree of interest.
[1]
[2]
[3]
[4]
[5]
Fig.2 Set of Rules Using the proposed implementation, the hidden association or interrelation between assorted aspects are fetched and evaluated. The distance between the various symptoms is calculated. dist(hairloss,sneeze) = 5 dist(weightloss,sneeze) = 5
[6]
[7]
[8]
dist(swallow,sneeze) = 5 dist(fatique,sneeze) = 4 dist(hairloss,headache) = 3
[9]
dist(weightloss,headache) = 3 dist(swallow,headache) = 3 dist (fatique,headache)= 3
[10]
dist (sneeze, fever) -> 3 dist (headache, viral) -> 3
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8. REFERENCES U. Fayyad, G. Piatetsky-Shapiro, P. Smyth,From data mining to knowledge discovery in databases,AI Mag., 17 (3) (1996), pp. 37–54. Agrawal, R., Imieliński, T., Swami, A., 1993. Mining association rules between sets of items in large databases”, ACM SIGMOD Record, vol. 22(2), pp. 207–216. B.Padmanabhan, A. Tuzhilin,On characterization and discovery of minimal unexpected patterns in rule discovery IEEE Trans. Knowl. Data Eng., 18 (2) (2006), pp. 202–216. B.Liu, W.Hsu, S.Chen, Y.Ma,Analyzing the subjective interestingness of association rules, Intelligent Sys. Appl. IEEE,15 (5) (2000), pp.47–55. Sahar,S., 2002. On incorporating subjective interestingness into the mining process, In Data Mining, 2002. ICDM 2003. Proceedings. Of the 2002 IEEE International Conference on Data Mining, pp. 681–684. K. McGarry,A survey of interestingness measures for knowledge discovery Knowl. Eng. Rev., 20 (01) (2005), pp. 39–61. R.Agrawal, T.Imielinski, A.Swami,Database mining: A performance perspective Knowl. Data Eng. IEEE Trans., 5 (6) (1993), pp. 914–925. R.Agrawal, R.Srikant, Fast algorithms for mining association rules, Proceedings of the 20th International Conference Very Large Data Bases, 1215 (1994), pp. 487–499. Farzanyar, Z., kangavari, M., Hashemi, S., 2006. A new algorithm for mining fuzzy association rules in the large databases based on ontology, Proceedings of the Sixth IEEE International Conference on Data Mining, pp. 65–69. B. Padmanabhan, A. Tuzhilin,Unexpectedness as a measure of interestingness in knowledge discovery Decision Support Sys., 27 (3) (1999), pp. 303–318.
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