Proceedings of the 8th International Conference on Intellectual ...

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2.2 Sequencing association rules Ridha Derrouiche et al. To discovery sequencing association rules (Agrawal, 1993) is a noteworthy data mining approach. There are some evidences <strong>of</strong> <strong>the</strong> trend <strong>of</strong> association rule in many business researches. This review indicated that association rule techniques used in various business, but ra<strong>the</strong>r rely on <strong>the</strong> popular used in supply chain management in performance measurement have not done very much. For example: depend on organizational strategies were mainly concentrated on improvement <strong>of</strong> customer service levels as well as reduction <strong>of</strong> operational costs in order to maintain pr<strong>of</strong>it margins. Therefore, Ko, et al. (2010) summarized <strong>the</strong> findings by a systematic review <strong>of</strong> existing research papers concerning <strong>the</strong> application <strong>of</strong> s<strong>of</strong>t computing techniques to supply chain management. The amount <strong>of</strong> studies in <strong>the</strong> supply chain management area using s<strong>of</strong>t computing approaches rose significantly and reached a peak in 2008, while some areas in supply chain management that had rarely been exposed in existing papers, such as customer relationship management and reverse logistics. So <strong>the</strong>y can bring <strong>the</strong> association rule data mining techniques to applications in <strong>the</strong> supply chain for example. The greater <strong>the</strong> uncertainties in supply and demand, globalization <strong>of</strong> <strong>the</strong> market in complex international supply network relationships have led to higher exposure to risks in <strong>the</strong> supply chain. Therefore, He and Song (2009) used association rules application for managing supply chain risk. Supplier selection is not only a significant work in supply chain management but also a complex decision making problem which includes both qualitative and quantitative factors. Xu and Lin (2009) viewed as <strong>the</strong> problem <strong>of</strong> ranking <strong>the</strong> candidate supplier and mining a large <strong>of</strong> database <strong>of</strong> shipment by using association rule mining. The paper has employed a numerical example for <strong>the</strong> integrated method for suitable supplier set. 2.3 K-Means algorithm K-Means is one <strong>of</strong> <strong>the</strong> modest unsupervised learning algorithms, solved <strong>the</strong> famous clustering problem. The procedure like this a basic practice to establish a given data set through a certain number <strong>of</strong> clusters (assume k clusters) fixed a priori (Ben-David et al, 2007 and Borah and Ghose, 2009). The chief idea is to outline k centroids, one for each cluster. These centroids should be placed in a cunning method on <strong>the</strong> ground <strong>of</strong> dissimilar site causes different result. So, <strong>the</strong> better choice is to place <strong>the</strong>m as much as possible far away from each o<strong>the</strong>r. The next step is to take each point belonging to a given data set and associate it to <strong>the</strong> nearest centroid. When no point is pending, <strong>the</strong> first step is completed and an early group age is done. At this point it is necessary to re-calculate k new centroids as bar centers <strong>of</strong> <strong>the</strong> clusters resulting from <strong>the</strong> previous step. After obtaining <strong>the</strong>se k new centroids, a new binding has to be done between <strong>the</strong> same data set points and <strong>the</strong> nearest new centroid. A loop has been generated. As a result <strong>of</strong> this loop, one may notice that <strong>the</strong> k centroids change <strong>the</strong>ir location step by step until no more changes are done. In o<strong>the</strong>r words centroids do not move any more. Finally, this algorithm aims at minimizing an objective function, in this case a squared error function 2.4 Particle swarm intelligence Particle swarm optimization (PSO) is a social behavior based stochastic optimization method developed by (Eberhart and Kennedy in 1995), At each step, each <strong>of</strong> <strong>the</strong>se particle locations is recorded to get a fitness value based on how well it can resolves <strong>the</strong> problem. By means <strong>of</strong> <strong>the</strong> local best position (Lbest : Pid) and <strong>the</strong> global best position (Gbest : Pgd), a novel velocity for each particle is updated by equation 1 Such f1 and f2 are called <strong>the</strong> coefficient <strong>of</strong> inertia, cognitive and society revision correspondingly. The rand ( ) stands uniformly distributed random numbers in [0,1]. The term Vid is restricted to <strong>the</strong> possible rang <strong>of</strong> Vmax . If <strong>the</strong> velocity disrupts this limit, it will be set at its proper limit. The concept <strong>of</strong> <strong>the</strong> updated velocity is illustrated in Figure 2. Varying velocity allows every particle to hunt around its distinct best position and global best position. Depended on <strong>the</strong> updated velocities, each particle changes its position according to equation 2 ( ) ( ) new old V = V + c 1⋅rand 1 P − X + c 2⋅rand 2 P −X new old new X = X + V id id id id gd id id id id (1) (2) Every particle is updated; <strong>the</strong> fitness value <strong>of</strong> each particle is recalculated. Providing that <strong>the</strong> fitness value <strong>of</strong> <strong>the</strong> new particle is superior to those <strong>of</strong> local best, formerly <strong>the</strong> local best will be substituted with <strong>the</strong> new particle. Supporting <strong>the</strong> fitness value <strong>of</strong> <strong>the</strong> new particle is higher than those <strong>of</strong> global 142
Ridha Derrouiche et al. best, <strong>the</strong>n <strong>the</strong> global best will be also replaced with <strong>the</strong> new particle. The procedure repeat above updating process step by step, <strong>the</strong> whole population evolves toward <strong>the</strong> optimum solution. Figure 3: The means <strong>of</strong> <strong>the</strong> updated velocity in <strong>the</strong> PSO 3. Methodology This proposed methodology for holistic performance enhancement road map using K-Means base on PSO and association rules for B2B-SCM context, as shown in Figure 4, <strong>the</strong> methodology is as follows. Parameters <strong>of</strong> PSO Population Size 50 Number <strong>of</strong> Iterations 200 Wmax Wmin C1 C2 0.9 0.4 1.2 1.2 Number Cluster 3 A. Main Research Step B. K-Means based on PSO Figure 4: Main flow chart <strong>of</strong> research methodology 143
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Proceedings <stron
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Contents Paper Title Author(s) Page
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Paper Title Author(s) Page No. Main
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Paper Title Author(s) Page No. Know
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Preface These proceedings represent
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TU Dortmund, Germany, ); Wing Bun L
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William Halal is Prof</stro
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Evgeny Blagov has obtained a specia
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science and mathem
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George Majdalany is a PhD candidate
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Joyce Nesamani Simson is a PhD cand
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Manpower Discipline Based on Knowle
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Peyman Akhavan et al. brains, knowl
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Peyman Akhavan et al. showed that o
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Peyman Akhavan et al. appropriate l
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Peyman Akhavan et al. sources for c
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Attitudes Towards Changes and Organ
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Ruth Alas et al. During the
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Professional inter
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Ruth Alas et al. Table 4: Correlati
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Research was supported by ETF Grant
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Rivadávia Correa Drummond de Alvar
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5. Conclusions Rivadávia Correa Dr
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Toward a Meta-Synthe</stron
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Xiaomi An et al 2.3 Reasons for dev
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Xiaomi An et al PTC works closely w
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Xiaomi An et al The first is an adm
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Xiaomi An et al policy reflections
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Figure 1: Transmission gear Marko A
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Marko Anzelak et al. knowledge basi
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Marko Anzelak et al. 72% recognise
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Marko Anzelak et al. virtually all
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Gabriel Prananingrum Aryanindita an
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Rita Babiliūtė and Kęstutis Kri
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Knowledge Management, Cultural Know
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Leonard Barnett and Edward Carter 2
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Leonard Barnett and Edward Carter p
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Leonard Barnett and Edward Carter p
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Leonard Barnett and Edward Carter d
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Impact of Knowledg
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Bob Barrett shorter product life-cy
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Bob Barrett 6. Concept of</
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Bob Barrett Brown, J.S. and Duguid
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Aurilla Aurelie Bechina and Eli Hus
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Page 113 and 114: Lorne Booker et al. Knowledge is us
Page 115 and 116: Figure 1: Business Valuation Knowle
Page 117 and 118: 3.4 Practitioner knowledge market L
Page 119 and 120: 4. Implications Lorne Booker et al.
Page 121 and 122: Strategic Thinking Dimensions in Em
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Page 125 and 126: Constantin Bratianu et al. Academy
Page 127 and 128: Constantin Bratianu et al. cases wi
Page 129 and 130: Constantin Bratianu et al. Pinker,
Page 131 and 132: Delio Ignacio Castañeda and Sandra
Page 137 and 138: Quality in Teacher Education: A Sys
Page 139 and 140: Rajendra Chetty The mechanism <stro
Page 141 and 142: Academic leadership Rajendra Chetty
Page 143 and 144: Rajendra Chetty There must be a re-
Page 145 and 146: Developing Personal Knowledge Manag
Page 147 and 148: Thirathep Chonmait
Page 149 and 150: Thirathep Chonmait
Page 151 and 152: The Relationship Between Knowledge
Page 153 and 154: Samuel Kai Wah Chu et al. A knowled
Page 155 and 156: Samuel Kai Wah Chu et al. negative
Page 157 and 158: Samuel Kai Wah Chu et al. Questionn
Page 159 and 160: Samuel Kai Wah Chu et al. few done
Page 161 and 162: Value Creation Through Collaborativ
Page 163: Ridha Derrouiche et al. level for e
Page 167 and 168: Ridha Derrouiche et al. experience
Page 169 and 170: Ridha Derrouiche et al. Of course,
Page 171 and 172: Organizational Learning and Transfo
Page 173 and 174: Irra Chrisyanti Dewi was carried ou
Page 175 and 176: Irra Chrisyanti Dewi Table 2: Valid
Page 177 and 178: Irra Chrisyanti Dewi Library is exp
Page 179 and 180: Knowledge Acquisition and Sharing:
Page 181 and 182: Ikechukwu Diugwu articulated and st
Page 183 and 184: Ikechukwu Diugwu establishing setti
Page 185 and 186: Ikechukwu Diugwu Mentzer, J. T., Mi
Page 187 and 188: Eva Eckenhofer cou
Page 189 and 190: Eva Eckenhofer The
Page 191 and 192: Eva Eckenhofer The
Page 193 and 194: Eva Eckenhofer Bur
Page 195 and 196: 1.2 Intellectual capital and innova
Page 197 and 198: 3.2.2 Reliability Irene Y.H. Fan an
Page 199 and 200: Figure 4: latent variable coefficie
Page 201 and 202: Irene Y.H. Fan and Rongbin W.B. Lee
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Page 205 and 206: 2.1 Appropriation Ronny Gey From a
Page 207 and 208: Ronny Gey Table 1: Characterization
Page 209 and 210: Ronny Gey The developers feel that
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Kenneth Grant The diffusion was stu
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Kenneth Grant A detailed computer-s
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Kenneth Grant did not come to <stro
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Kenneth Grant of m
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Michel Grundstein although hundred
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Michel Grundstein Figure 1: DITEK p
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Michel Grundstein 4.1.1 Knowledge i
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Michel Grundstein share. However <s
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6. Conclusions and perspectives Mic
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Intellectual Property Valuation Dec
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Hezlin Harris et al. (Burrone and S
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Hezlin Harris et al. The need for s
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Acknowledgements Hezlin Harris et a
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Managing an Assessment Process <str
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Table 1: List of N
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Shaheed Hartley Table 3: OEQ respon
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Shaheed Hartley It is very difficul
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A Community Framework for Knowledge
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Igor Hawryszkiewycz between communi
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Figure 4: Modeling knowledge subsys
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Igor Hawryszkiewycz Prahalad, C.K.,
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Eli Hustad and Aurilla Aurelie Bech
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Does Knowledge Stickiness Affect a
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Nurul Indarti knowledge (Nelson and
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3. Research methods 3.1 Research in
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Coded knowledge Nurul Indarti The f
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Nurul Indarti Arrow, K. J. (1969).
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Organizational Learning in Pr<stron
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Brigita Janiunaite et al. learn and
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Brigita Janiunaite et al. given dif
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Brigita Janiunaite et al. empowered
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Brigita Janiunaite et al. A statist
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Brigita Janiunaite et al. learning
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Balancing the Ince
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Giedrius Jucevičius safeguard and
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Giedrius Jucevičius nature <strong
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Giedrius Jucevičius motivating fac
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Giedrius Jucevičius Ghosh, T. (200
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Palmira Juceviciene and Vyda Mozuri
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Robertas Jucevicius relations shoul
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Robertas Jucevicius do not know whe
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Robertas Jucevicius and innovation
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Robertas Jucevicius Lawton Smith, H
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Annukka Jyrämä et al. knowledge c
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Annukka Jyrämä et al. Respondents
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Annukka Jyrämä et al. various cit
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Annukka Jyrämä et al. example hea
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Managing Information Overload - Tea
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Harri Ketamo conceptual structure a
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Harri Ketamo limited to YouTube, Fl
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Harri Ketamo Figure 8: Visualizatio
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Farmers’ Knowledge Management in
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2.1 Farmer knowledge Le Nguyen Doan
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Le Nguyen Doan Khoi days needed for
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Le Nguyen Doan Khoi dosage. Moreove
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Le Nguyen Doan Khoi use homemade in
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Developing Human Capital through Ne
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Eric Kong organisation as individua
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Eric Kong As described by Tosey and
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Eric Kong Craft, A. (2001) "Neuro-l
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Sophie Le Bellu and Benoit Le Blanc
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Results Sophie Le Bellu and Benoit
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Pornpen Lertthasanawong and Nopasit
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2. Literature review Ya-Hui Ling 2.
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Ya-Hui Ling action of</stro
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Intellectual Capital Intellectual C
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Ya-Hui Ling Hitt, M.A., Hoskisson,
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Maksim Maksimovic et al paper prese
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Maksim Maksimovic et al 4. Lean kno
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Maksim Maksimovic et al of<
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Maurizio Massaro and Andrea Moro <s
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Maurizio Massaro and Andrea Moro Pr
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Maurizio Massaro and Andrea Moro hi
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Maurizio Massaro and Andrea Moro Si
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Helen Mitchell and Dennis Viehland
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Figure 1: KMS Framework for collabo
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Mohd Zali Mohd Nor et al. Ano<stron
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Mohd Zali Mohd Nor et al. Table 4:
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Mohd Zali Mohd Nor et al. KQ8 provi
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Maria Cristina Morariu economy and
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Table 1: Literature review summary
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Maria Cristina Morariu ATO = Turnov
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4.2 Correlation analysis Maria Cris
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4.3 Regression analysis Maria Crist
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Table 13: Regression analysis_M6 Mo
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The Arrival of <st
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Bertrand Pauget and Ahmed Dammak Mo
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Bertrand Pauget and Ahmed Dammak no
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Bertrand Pauget and Ahmed Dammak th
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Bertrand Pauget and Ahmed Dammak Sa
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Wannapa Pipattanawong et al. cannot
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Wannapa Pipattanawong et al. 3.2 Do
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Wannapa Pipattanawong et al. Moreov
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The Dark Side of S
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Siwarit Pongsakornrungsilp vans, ro
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Siwarit Pongsakornrungsilp Kreng Ja
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Siwarit Pongsakornrungsilp Lave, J.
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