INEFFICIENCY OF DATA MINING ALGORITHMS AND ITS ARCHITECTURE: WITH EMPHASIS TO THE SHORTCOMING OF DATA MINING ALGORITHMS ON THE OUTPUT OF THE RESEARCHES
Workineh TESEMA
workineh.tesema@ju.edu.etJimma University, Faculty of Computing, Department of Information Technology, Jimma (Ethiopia)
Abstract
This review paper presents a shortcoming associated to data mining algorithm(s) classification, clustering, association and regression which are highly used as a tool in different research communities. Data mining researches has successfully handling large amounts of dataset to solve the problems. An increase in data sizes was brought a bottleneck on algorithms to retrieve hidden knowledge from a large volume of datasets. On the other hand, data mining algorithm(s) has been unable to analysis the same rate of growth. Data mining algorithm(s) must be efficient and visual architecture in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. The increasing use of information visualization tools (architecture) and data mining algorithm(s) stems from two separate lines of research. Data visualization researchers believe in the importance of giving users an overview and insight into the data distributions. Many powerful visual graphical interfaces are built on top of statistical analysis and data mining algorithms to permit users to leverage their power without a deep understanding of the underlying technology. The combination of the graphical interface is permit to navigate through the complexity of statistical and data mining techniques to create powerful models. Therefore, there is an increasing need to understand the bottlenecks associated with the data mining algorithms in modern architectures and research community. This review paper basically to guide and help the researchers specifically to identify the shortcoming of data mining techniques with domain area in solving a certain problems they will explore. It also shows the research areas particularly a multimedia (where data can be sequential, audio signal, video signal, spatio-temporal, temporal, time series etc) in which data mining algorithms not yet used.
Keywords:
Data Mining, classification, clustering, association, regression, algorithms bottleneckReferences
Agrawal, R., & Srikant, R. (2015). Fast algorithms for mining association rules. In Proc. of the 20th International Conference on Very Large Data Bases (VLDB) (pp. 487–499). Santiago, Chile.
Google Scholar
Al-Khoder, A., & Harmouch, H. (2015). Evaluating Four Of The most Popular Open Source and Free Data Mining Tools. International Journal of Academic Scientific Research, 3(10), 13–23.
Google Scholar
Bavisi, S., Mehta, J., & Lopes, L. (2014). A Comparative Study of Different Data Mining Algorithms. International Journal of Current Engineering and Technology, 4(5), 3248–3252.
Google Scholar
Gulli, A., & Pal, S. (2017). Deep Learning with Keras-Implement neural networks with Keras on Theano and Tensor Flow. Birmingham, UK: Packt Publishing.
Google Scholar
Huang, H. C., & Hou, C. I. (2017). Tourism Demand Forecasting Model Using Neural Network. International Journal of Computer Science & Information Technology (IJCSIT), 9(2), 19–29.
DOI: https://doi.org/10.5121/ijcsit.2017.9202
Google Scholar
Joseph, S. R., Hlomani, H., & Letsholo, K. (2016). Data Mining Algorithms: An Overview. International journal of Computers and Technology, 15(6), 6806–6813.
DOI: https://doi.org/10.24297/ijct.v15i6.1615
Google Scholar
Kalyani, J., Bharathi, H. N., & Rao, J. (2016) Stock Trend Prediction Using News Sentiment Analysis, International Journal of Computer Science & Information Technology (IJCSIT), 8(3), 67–76.
DOI: https://doi.org/10.5121/ijcsit.2016.8306
Google Scholar
Kotu, V., & Deshpande, B. (2015). Predictive Analytics and Data Mining – Concepts and Practice with RapidMiner. Elsevier.
DOI: https://doi.org/10.1016/B978-0-12-801460-8.00013-6
Google Scholar
Kumbhare, T. A., & Chobe, S. V. (2014) An Overview of Association Rule Mining Algorithms. International Journal of Computer Science and Information Technologies, 5(1), 927–930.
Google Scholar
Massaro, A., Barbuzzi, D., Vitti, V., Galiano, A., Aruci, M., & Pirlo, G. (2016), Predictive Sales Analysis According to the Effect of Weather. In Proceeding of the 2nd International Conference on Recent Trends and Applications in Computer Science and Information Technology (pp. 53–55). Tirana, Albania.
Google Scholar
Massaro, A., Galiano, A., Barbuzzi, D., Pellicani, L., Birardi, G., Romagno, D. D., & Frulli, L., (2017). Joint Activities of Market Basket Analysis and Product Facing for Business Intelligence oriented on Global Distribution Market: examples of data mining applications. International Journal of Computer Science and Information Technologies, 8(2), 178–183.
Google Scholar
Massaro, A., Maritati, V., & Galiano, A. (2018). Data Mining Model Performance of Sales Predictive Algorithms Based On Rapidminer Workflows. International Journal of Computer Science & Information Technology (IJCSIT), 10 (3) 39–56. https://doi.org/10.5121/ijcsit.2018.10303
DOI: https://doi.org/10.5121/ijcsit.2018.10303
Google Scholar
Negandhi, G. (2007). Apriori Algorithm Review for Finals (SE 157B). Spring Semester. Nguyen, H.-L., Woon, Y. K., & Ng, W. K. (2015). A Survey on Data Stream Clustering and Classification. Knowledge and Information Systems, 45(3), 535–569. https://doi.org/10.1007/s10115-014-0808-1
DOI: https://doi.org/10.1007/s10115-014-0808-1
Google Scholar
Otha, M., & Higuci, Y. (2013). Study on Design of Supermarket Store Layouts: the Principle of Sales Magnet, World Academy of Science. Engineering and Technology, 7(1), 209–212.
Google Scholar
Ozisikyilmaz, B. (2009). Analysis, Characterization and Design of Data Mining Applications and Applications to Computer Architecture (Unpublished doctoral dissertation). Northwestern University, Evanston, Illinois.
Google Scholar
Rehman, N. (2017). Data Mining Techniques Methods Algorithms and Tools. International of Computer Science and Mobile Computing, 6(7), 227–231.
Google Scholar
Shneiderman, B. (2003). Inventing discovery tools: Combining information visualization with data mining. In The Craft of Information Visualization Readings and Reflections Interactive Technologies (pp.378-385). Morgan Kaufmann. https://doi.org/10.1016/B978-155860915-0/50048-2
DOI: https://doi.org/10.1016/B978-155860915-0/50048-2
Google Scholar
Štulec, I., Petljak, K., & Kukor, A. (2016). The Role of Store Layout and Visual Merchandising in Food Retailing. European Journal of Economics and Business Studies, 4(1), 139–152.
DOI: https://doi.org/10.26417/ejes.v4i1.p138-151
Google Scholar
Swarndeep Saket, J., & Pandya, S. (2016). An Overview of Partitioning Algorithms in Clustering Techniques. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 5(6), 1943–1946.
Google Scholar
Talia, D., Trunfio, P., & Marozzo, F. (2016). Data Analysis in the Cloud: Models and Techniques for Cloud-Based Data Analysis. Elsevier Science.
DOI: https://doi.org/10.1016/B978-0-12-802881-0.00003-2
Google Scholar
Wimmer, H., & Powell, L. M. (2015) A Comparison of Open Source Tools for Data Science, In Proceedings of the Conference on Information Systems Applied Research (v8 n3651). Wilmington, North Carolina USA.
Google Scholar
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J., & Steinberg, D. (2007). Top 10 algorithms in data mining. London, UK: Springer-Verlag London Limited.
DOI: https://doi.org/10.1007/s10115-007-0114-2
Google Scholar
Xu, D., & Tian, Y. (2015). A Comprehensive Survey of Clustering Algorithms. Annals of Data Science, 2(2), 165–193. doi:10.1007/s40745-015-0040-1
DOI: https://doi.org/10.1007/s40745-015-0040-1
Google Scholar
Yadav, Ch., Wang, S., & Kumar, M. (2013) Algorithm and approaches to handle large Data-A Survey, International Journal of Computer Science and Network, 2(3), 1307.5437.
Google Scholar
Zafarani, R., Abbasi, M., & Liu, H. (2014). Social Media Mining. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139088510
DOI: https://doi.org/10.1017/CBO9781139088510
Google Scholar
Authors
Workineh TESEMAworkineh.tesema@ju.edu.et
Jimma University, Faculty of Computing, Department of Information Technology, Jimma Ethiopia
Statistics
Abstract views: 77PDF downloads: 4
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Pannangi Naresh, R. Suguna, IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES , Applied Computer Science: Vol. 17 No. 3 (2021)
- Sri INDRA MAIYANTI, Anita DESIANI, Syafrina LAMIN, P PUSPITAHATI, Muhammad ARHAMI, Nuni GOFAR, Destika CAHYANA, ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Leszek JASKIERNY, REVIEW OF THE DATA MODELING STANDARDS AND DATA MODEL TRANSFORMATION TECHNIQUES , Applied Computer Science: Vol. 14 No. 4 (2018)
- Gamze Ogcu KAYA, Ali TURKYILMAZ, INTERMITTENT DEMAND FORECASTING USING DATA MINING TECHNIQUES , Applied Computer Science: Vol. 14 No. 2 (2018)
- Jolanta BRZOZOWSKA, Jakub PIZOŃ, Gulzhan BAYTIKENOVA, Arkadiusz GOLA, Alfiya ZAKIMOVA, Katarzyna PIOTROWSKA, DATA ENGINEERING IN CRISP-DM PROCESS PRODUCTION DATA – CASE STUDY , Applied Computer Science: Vol. 19 No. 3 (2023)
- K. Raju, Niranjan N Chiplunkar, PERFORMANCE ENHANCEMENT OF CUDA APPLICATIONS BY OVERLAPPING DATA TRANSFER AND KERNEL EXECUTION , Applied Computer Science: Vol. 17 No. 3 (2021)
- Amina ALYAMANI, Oleh YASNIY, CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 16 No. 4 (2020)
- Baldemar ZURITA, Luís LUNA, José HERNÁNDEZ, Federico RAMÍREZ, BOVW FOR CLASSIFICATION IN GEOMETRICS SHAPES , Applied Computer Science: Vol. 14 No. 4 (2018)
- Sheikh Amir FAYAZ, Majid ZAMAN, Muheet Ahmed BUTT, Sameer KAUL, HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Rosa Maria VAZQUEZ, Edmundo BONILLA, Eduardo SANCHEZ, Oscar ATRIANO, Cinthya BERRUECOS, APPLICATION OF DATA MINING TECHNIQUES TO FIND RELATIONSHIPS BETWEEN THE DISHES OFFERED BY A RESTAURANT FOR THE ELABORATION OF COMBOS BASED ON THE PREFERENCES OF THE DINERS , Applied Computer Science: Vol. 15 No. 2 (2019)
You may also start an advanced similarity search for this article.
