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International Journal of Data Engineering (IJDE)
An International peer-review journal operated under CSC-OpenAccess Policy.
ISSN - 2180-1274
Published - Bi-Monthly   |   Established -    |   Year of Publication - 2018

SUBMISSION
April 30, 2019

NOTIFICATION
May 31, 2019

PUBLICATION
June 30, 2019

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CITATION ANALYSIS

IJDE Citation Impact
(69 - 0) / 31 = 2.226

Refer to Citation Report for 2018 for complete details.
 

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CITATION REPORT FOR IJDE

Below calculations are based on in-process citations that are extracted through Google Scholar.


Total Citations = 69
Self Citations = 0
Total Publications = 31


Citation Impact
(Total Citations - Self Citations) / Total Publications

Citation Impact
(69 - 0) / 31 = 2.226

 
SR
M-CODE
CITATION
1
Duong, T. V. T., Do, T. D., & Nguyen, N. P. (2015, July). Exploiting faculty evaluation forms to improve teaching quality: An analytical review. In Science and Information Conference (SAI), 2015 (pp. 457-462). IEEE.
2
Lim, J. H., & Lee, K. C. (2015). Classifying Biomedical Literature Providing Protein Function Evidence. ETRI Journal, 37(4), 813-823.
3
Barros, V. F. D. A., Ramos, I., & Perez, G. (2015). information systems and organizational memory: a literature review. JISTEM-Journal of Information Systems and Technology Management, 12(1), 45-63.
4
Redding, L. (2015). Through-Life Engineering Services: Definition and Scope: A Perspective from the Literature. In Through-life Engineering Services (pp. 13-28). Springer International Publishing.
5
Redding, L. E., Hockley, C. J., Roy, R., & Menhen, J. (2015). The Role of Maintenance, Repair, and Overhaul (MRO) Knowledge in Facilitating Service Led Design: A Nozzle Guide Vane Case Study. In 9th WCEAM Research Papers (pp. 379-395). Springer International Publishing.
6
Asayesh, A., Hadavi, M. A., & Jalili, R. (2015). (t, k)-Hypergraph anonymization: an approach for secure data publishing. Security and Communication Networks, 8(7), 1306-1317.
7
Rupnik, J., Davies, J., Fortuna, B., Duke, A., & Clarke, S. S. (2015, October). Travel Time Prediction on Highways. In Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on (pp. 1435-1442).
8
Hung, L. N., Thu, T. N. T., & Nguyen, G. C. (2015). An Efficient Algorithm in Mining Frequent Itemsets with Weights over Data Stream Using Tree Data Structure. International Journal of Intelligent Systems and Applications (IJISA), 7(12), 23.
9
Brar, S., Mathur, D., Sharma, N., & Phagwara, P. Enhancement in Semantic based Model for Text Document Clustering.
10
Shyamala, V. S., & Christopher, T. (2015). Managing Privacy of Sensitive Attributes Using MFSARNN Clustering with Optimization Technique. International Review on Computers and Software (IRECOS), 10(9), 907-911.
11
Parlak, B., & Uysal, A. K. (2015, May). Classification of medical documents according to diseases. In Signal Processing and Communications Applications Conference (SIU), 2015 23th (pp. 1635-1638). IEEE.
12
Shah, P. R., Vaghela, D. B., & Sharma, P. (2015, March). Faculty performance evaluation based on prediction in distributed data mining. In Engineering and Technology (ICETECH), 2015 IEEE International Conference on (pp. 1-5). IEEE.
13
Kalibataite, G. (2014). Integration of activity data: problems, peculiarities and the importance of metadata. Socialines Technologijos, 4(1).
14
Kalibataite, G. (2014). Veiklos duomenu integravimas: problemos, ypatumai ir metaduomenu svarba. Social Technologies, (01), 93-117.
15
Drakshayania, B., & Prasad, E. V. Hybrid Clustering Model for Text Documents with Semantic Based Document Representation.
16
Shaari, F., Ahmad, A., & ALong, Z. (2014). Outlier Detection Method based on Hybrid Rough-Negative Algorithm. Journal of Mathematics and System Science, 4(6).
17
Suguna, N. (2014). Certain investigations on Classification of medical datasets Using soft computing techniques.
18
Kanakam, P., Gupta, S., Hussain, S. M., & Narayana, D. S. (2014). An Analysis of Exploring Information from Search Engines in Semantic Manner. International Journal, 4(5).
19
Tâmbulea, l., darabant, a. s., & varga, v. (2014). data transfer optimization in distributed database query processing. studia universitatis babes-bolyai, informatica, 59(1).
20
Long, A. (2014) Outlier Detection Method based on Hybrid Rough-Negative Algorithm Mathematics and Systems Science: English, 4 (6), 391-397.
21
Kotulla, A. (2012). Negative associacion rules–computing, measures and application areas. Studia Informatica, 33(2B), 273-285.
22
Ahmad, A., Shaari, F., & Long, Z. A. (2014, January). Outlier detection method based on hybrid rough: negative using PSO algorithm. In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (p. 108). ACM.
23
Khan, I. A., & Choi, J. T. (2014). An Application of Educational Data Mining (EDM) Technique for Scholarship Prediction. International Journal of Software Engineering and Its Applications, 8(12), 31-42.
24
Singh, A. K., Tondon, S. R., & Diwan, T. D. One Time Mining by Multi-Core Preprocessing on Generalized Dataset.
25
Sharma, S. K., & Ugrasen, S. (2014). A trust-based architectural framework for collaborative filtering recommender system. International Journal of Business Information Systems, 16(2), 134-153.
26
Devi, M. K., & Rani, M. U. Fuzzy Weighted Associative Classifier based on Positive and Negative Rules.
27
Mala, A., & Ramesh, D. F. (2014, August). Web Log Mining to Enhance Surfing Experience. In Applied Mechanics and Materials (Vol. 626, pp. 7-13). Trans Tech Publications.
28
binti Dollah, R., & Aono, M. (2014). Employing Ontology Enrichment Algorithm in Classifying Biomedical Text Abstracts.
29
Mathai, P. P., & Balan, R. S. An Extensive Review of Significant Researches in Data Mining.
30
Drakshayani, B., & Prasad, E. V. (2013). Semantic Based Model for Text Document Clustering with Idioms. Intl. J. Date Engg, 4(1), 1-13.
31
Nguyen, T. T., & Nguyen, P. K. (2013). A New Viewpoint for Mining Frequent Patterns. Editorial Preface, 4(3).
32
Roongkaew, W., & Prompoon, N. (2013, September). Software engineering tools classification based on SWEBOK taxonomy and software profile. In Informatics and Applications (ICIA), 2013 Second International Conference on (pp. 122-128). IEEE.
33
Sharma, S. K., & Suman, U. (2013). A framework of hybrid recommender system for web personalisation. International Journal of Business Information Systems, 13(3), 284-316.
34
Sharma, S. K., & Suman, U. (2013). An efficient semantic clustering of URLs for web page recommendation. International Journal of Data Analysis Techniques and Strategies, 5(4), 339-358.
35
Ali, S. Z., & Rathore, Y. A comprehensive study of major techniques of multi level frequent pattern mining: a survey.
36
Tahir, d. n. international journal of data engineering (ijde).
37
Bangalore, M. H. S. (2012). Resource adaptive technique for frequent itemset mining in transactional data streams. IJCSNS, 12(10), 87.
38
Drakshayani, B., & Prasad, E. V. Metaphor based Document Representation Model for Text Document Clustering. In IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (pp. 74-78).
39
Aher, S., & Lobo, L. M. R. J. (2012). Mining association rule in classified data for course recommender system in e-learning. International Journal of Computer Applications, 39(7), 1-7.
40
Kotulla, A. (2012). Negatywne reguly asocjacyjne-wyznaczanie, miary i obszary zastosowania. Studia Informatica, 33(2B), 273-285.
41
Li Haifeng, Zhang Ning, Zhu Jianming, & Caohuai Hu. (2012) itemsets time-sensitive data stream mining algorithms. Journal of Computers, 35 (11), 2283-2293.
42
Nguyen, T. T., & Nguyen, P. K. (2012). A new approach for problem of sequential pattern mining. In Computational Collective Intelligence. Technologies and Applications (pp. 51-60). Springer Berlin Heidelberg.
43
Chandrika, J., & Kumar, K. A. (2012). Frequent Itemset Mining in Transactional Data Streams Based on Quality Control and Resource Adaptation. International Journal of Data Mining & Knowledge Management Process, 2(6), 1.
44
Suneetha, s., & rani, m. u. status quo of semantic-based text document clustering: a review.
45
Patil, g. y., shahade, a. k., & bamnote, d. g. r. (2012). international journal of pure and applied research in engineering and technology. ret, 1(1), 1-7.
46
B Aher, S., & LMRJ, L. (2012). Combination of clustering, classification & association rule based approach for course recommender system in E-learning. International Journal of Computer Applications, 39(7), 8-15.
47
Verma, G., & Nanda, V. (2012). Frequent Item set Generation by Parallel Preprocessing on Generalized Dataset. International Journal of Scientific & Engineering Research, 3(4).
48
Sharma, S. K., & Suman, U. (2012, September). Comparative study and analysis of web personalization frameworks of recommender systems for e-commerce. In Proceedings of the CUBE International Information Technology Conference (pp. 629-634). ACM.
49
Verma, G., & Nanda, V. (2012). Association Rule Mining by Block Scattered Transposition. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(3), pp-99.
50
Nguyen, Q. D. (2012). The role of business intelligence in organizational memory supporti.
51
Rölker-Denker, L., & Hein, A. (2012). Organisationales Lernen und Organisationale Gedächtnisse im AAL-Kontext. Technik für ein selbstbestimmtes Leben.
52
Herawan, T., Noraziah, A., Abdullah, Z., Deris, M. M., & Abawajy, J. H. (2012). EFP-M2: efficient model for mining frequent patterns in transactional database. In Computational Collective Intelligence. Technologies and Applications (pp. 29-38). Springer Berlin Heidelberg.
53
Diwakar, S., Singhai, R., & Thakur, N. S. power constant based methods for dealing with missing values in knowledge discovery.
54
Verma, G., & Nanda, V. (2012, March). An Effectual Algorithm For Frequent Itemset Generation In Generalized Data Set Using Parallel Mesh Transposition. In Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on (pp. 719-724). IEEE.
55
Aher, S. B., & Lobo, L. M. R. (2012). Best combination of machine learning algorithms for course recommendation system in e-learning. International Journal of Computer Applications, 41(6).
56
SU, Y. R., WANG, R. J., Peng, C. H. E. N., WEI, Y. Y., LI, C. X., & HU, Y. M. (2012). Agricultural ontology based feature optimization for agricultural text clustering. Journal of Integrative Agriculture, 11(5), 752-759.
57
Kuo, C. C., & Shieh, H. L. (2012). A Semi-Supervised Learning Algorithm for Data Classification. International Journal of Pattern Recognition and Artificial Intelligence, 1551007.
58
Abbas, M., & Shoukry, A. (2012, July). CMUNE: A clustering using mutual nearest neighbors algorithm. In Information Science, Signal Processing and their Applications (ISSPA), 2012 11th International Conference on (pp. 1192-1197). IEEE.
59
Shukla, D., Verma, K., & Gangele, S. re-attempt connectivety to internet analysis of user by markov chain model. chief patron chief patron.
60
Sharma, S. K., & Suman, U. (2011). Analysis of Frequent URLs for a Recommender System Using Performance Based Transposition Algorithm. Automation and Autonomous System, 3(11), 526-532.
61
Sharma, S. K., & Suman, U. (2011). Design and Implementation of Architectural Framework of Recommender System for e-Commerce. International Journal of Computer Science and Information Technology & Security (IJCSITS), 1(2), 153-162.
62
Gangele, S., Shukla, D., Verma, K., & Singh, P. (2011). Elasticities and Index Analysis of Usual Internet Traffic Share Problem. International Journal of Advanced Research in Computer Science, 2(4).
63
Shukla, D., Singhai, R., & Thakur, N. S. (2011). A New Imputation Method for Missing Attribute Values in Data Mining. Journal of Applied Computer Science & Mathematics, (10).
64
Liukkonen, M. (2010). Intelligent Methods in the Electronics Industry. University of Eastern Finland.
65
El-Yazeed, N. A. A Survey on Web Recommendation Systems Based on Web Usage Mining.
66
Liu, L., Wang, Y., & Liu, S. (2013). Model of opinion interactions base on evolutionary game in social network. In Intelligent Computing Theories and Technology (pp. 64-72). Springer Berlin Heidelberg.
67
Bajaj, P., & Raheja, S. (2014). A Vague Improved Markov Model Approach for Web Page Prediction. arXiv preprint arXiv:1405.7868.
68
Thwe, P. Using Markov Model and Popularity and Similarity-based Page Rank Algorithm for Web Page Access Prediction.
69
Liua, L., Lvb, J., Lic, J., & Wangc, Y. Evolutionary Game Model of Information Interaction in Social Network?.