Journal articles: 'Data Mining Approaches'

1

Mamitsuka, Hiroshi. "Glycoinformatics: Data Mining-based Approaches." CHIMIA International Journal for Chemistry 65, no. 1 (February 23, 2011): 10–13. http://dx.doi.org/10.2533/chimia.2011.10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Xiao, Wenke, Lijia Jing, Yaxin Xu, Shichao Zheng, Yanxiong Gan, and Chuanbiao Wen. "Different Data Mining Approaches Based Medical Text Data." Journal of Healthcare Engineering 2021 (December 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/1285167.

Full text

Abstract:

The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

APA, Harvard, Vancouver, ISO, and other styles

3

Meng, Jun, Xiao Chen, Tian Yu Zhu, and Yang Yang Pan. "Data Mining Approaches in Manpower Evaluation." Applied Mechanics and Materials 513-517 (February 2014): 750–53. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.750.

Full text

Abstract:

Manpower allocation determines the competence of the companies. Scientific manpower allocation calls for accurate evaluation on the abilities that the employees have for the posts. In this paper, we first present a general fuzzy clustering model for manpower evaluation for companies. To verify the approach, a new distance-based evaluation model is also presented. Simulation results demonstrated the accuracy of our research.

APA, Harvard, Vancouver, ISO, and other styles

4

Cordero, F., M. Botta, and R. A. Calogero. "Microarray data analysis and mining approaches." Briefings in Functional Genomics and Proteomics 6, no. 4 (January 22, 2008): 265–81. http://dx.doi.org/10.1093/bfgp/elm034.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Böcker, Alexander, Gisbert Schneider, and Andreas Teckentrup. "Status of HTS Data Mining Approaches." QSAR & Combinatorial Science 23, no. 4 (June 2004): 207–13. http://dx.doi.org/10.1002/qsar.200330860.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 14, no. 3-4 (February 2009): 147–54. http://dx.doi.org/10.1016/j.drudis.2008.12.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 17 (February 2012): S16—S23. http://dx.doi.org/10.1016/j.drudis.2011.12.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (January 2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

Full text

Abstract:

The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

APA, Harvard, Vancouver, ISO, and other styles

9

SRIRAAM, N., V. NATASHA, and H. KAUR. "DATA MINING APPROACHES FOR KIDNEY DIALYSIS TREATMENT." Journal of Mechanics in Medicine and Biology 06, no. 02 (June 2006): 109–21. http://dx.doi.org/10.1142/s0219519406001893.

Full text

Abstract:

Data mining techniques has been used as a recent trend for achieving diagnostics results, especially in medical fields such as kidney dialysis, skin cancer and breast cancer detection, and also biological sequences classification. Due to its ability to discover the relationship and pattern of the medical database, early detection or prediction of pathological conditions through mining has become feasible. This paper discusses the data mining approach for parametric evaluation to improve the treatment of kidney dialysis patient. The experimental result shows that classification accuracy using Association mining between the ranges 50–97.7% is obtained based on the dialysis parameter combination. Such a decision-based approach helps the clinician to decide the level of dialysis required for individual patient.

APA, Harvard, Vancouver, ISO, and other styles

10

Fridman, Olga V. "Data Mining - methods and algorithms, summary." Transaction Kola Science Centre 12, no. 5-2021 (December 27, 2021): 91–103. http://dx.doi.org/10.37614/2307-5252.2021.5.12.008.

Full text

Abstract:

The article provides a brief overview of Data Mining methods and algorithms which are used in solving various tasks where both quantitative and qualitative data have to be processed. The purpose of the review is a brief description of the methods and algorithms, as well as a list of sources in which they are described in detail. The features of existing approaches to solving such problems are considered, the analysis of modern methods for solving Data Mining problems is carried out.

APA, Harvard, Vancouver, ISO, and other styles

11

Nayak, Suryakanta, and Mrutyunjaya Panda. "VLSI Cell Partitioning Using Data Mining Approaches." International Journal of Computer Sciences and Engineering 6, no. 8 (August 31, 2018): 1019–27. http://dx.doi.org/10.26438/ijcse/v6i8.10191027.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Obstfeld, Amrom E., Khushbu Patel, James C. Boyd, Julia Drees, Daniel T. Holmes, John P. A. Ioannidis, and Arjun K. Manrai. "Data Mining Approaches to Reference Interval Studies." Clinical Chemistry 67, no. 9 (August 17, 2021): 1175–81. http://dx.doi.org/10.1093/clinchem/hvab137.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Blockeel, Hendrik. "Data Mining: From Procedural to Declarative Approaches." New Generation Computing 33, no. 2 (April 2015): 115–35. http://dx.doi.org/10.1007/s00354-015-0202-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Uçar, Tamer, and Adem Karahoca. "Benchmarking data mining approaches for traveler segmentation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 409. http://dx.doi.org/10.11591/ijece.v11i1.pp409-415.

Full text

Abstract:

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.

APA, Harvard, Vancouver, ISO, and other styles

15

Homayoun, Sajad, and Marzieh Ahmadzadeh. "A review on data stream classification approaches." Journal of Advanced Computer Science & Technology 5, no. 1 (February 7, 2016): 8. http://dx.doi.org/10.14419/jacst.v5i1.5225.

Full text

Abstract:

<p>Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all instances to be labeled for creating classifier models, but there are some methods (Semi-Supervised Learning and Active Learning) in which unlabeled data is employed as well as labeled data. In this paper, by focusing on ensemble methods, semi-supervised and active learning, a review on some state of the art researches is given.</p>

APA, Harvard, Vancouver, ISO, and other styles

16

Dalal, Vandna Dahiya and Sandeep. "Parallel Approaches of Utility Mining for Big Data." Webology 17, no. 2 (December 21, 2020): 31–43. http://dx.doi.org/10.14704/web/v17i2/web17014.

Full text

Abstract:

Utility Itemset Mining (UIM) is a fundamental technique to find out various itemsets with interestingness measures in addition to their quantity. It helps in finding valuable items that cannot be tracked with frequent itemset mining. There are many techniques to mine the itemsets based on their utilities, but the need of the hour is to mine them from larger datasets. This paper presents a brief overview of various approaches for utility mining, which mine using the parallel framework to enhance the pace of computation. The paper is concluded with a discussion on various challenges and openings in the field of parallel mining and provides away for further development of the prevailing methodologies of big data.

APA, Harvard, Vancouver, ISO, and other styles

17

Wilson, Seunghye J. "Data representation for time series data mining: time domain approaches." Wiley Interdisciplinary Reviews: Computational Statistics 9, no. 1 (December 25, 2016): e1392. http://dx.doi.org/10.1002/wics.1392.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Ali, Ammar Alhaj, Pavel Varacha, Said Krayem, Petr Zacek, and Andrzej Urbanek. "Distributed data mining systems: techniques, approaches and algorithms." MATEC Web of Conferences 210 (2018): 04038. http://dx.doi.org/10.1051/matecconf/201821004038.

Full text

Abstract:

Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so with the wide availability of huge amounts of data; necessarily we are become in need for turning this data into useful information and knowledge, where Data mining uncovers interesting patterns and relationships hidden in a large volume of raw data and big data is a new term used to identify the datasets that are of large size and have grater complexity. The knowledge gained from data can be used for applications such as market analysis, customer retention and production control. Data mining is a massive computing task that deals with huge amount of stored data in a centralized or distributed system to extract useful information or knowledge. In this paper, we will discuss Distributed Data Mining systems, approaches, Techniques and algorithms to deal with distributed data to discover knowledge from distributed data in an effective and efficient way.

APA, Harvard, Vancouver, ISO, and other styles

19

Fabrianne, Syifa Faradilla, Agung Triayudi, and Ira Diana Sholihati. "Data mining using filtering approaches and ensemble methods." IOP Conference Series: Materials Science and Engineering 1088, no. 1 (February 1, 2021): 012012. http://dx.doi.org/10.1088/1757-899x/1088/1/012012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Aouad, Lamine M., Nhien An-Lekhac, and Tahar Kechadi. "Grid-Based Approaches for Distributed Data Mining Applications." Journal of Algorithms & Computational Technology 3, no. 4 (December 2009): 517–34. http://dx.doi.org/10.1260/174830109789621374.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Chaki, Jyotismita, Nilanjan Dey, B. K. Panigrahi, Fuqian Shi, Simon James Fong, and R. Simon Sherratt. "Pattern Mining Approaches Used in Social Media Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp02 (December 2020): 123–52. http://dx.doi.org/10.1142/s021848852040019x.

Full text

Abstract:

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

APA, Harvard, Vancouver, ISO, and other styles

22

Ososkov, Gennady A. "Novel approaches of data-mining in experimental physics." Tatra Mountains Mathematical Publications 51, no. 1 (November 1, 2012): 131–40. http://dx.doi.org/10.2478/v10127-012-0013-0.

Full text

Abstract:

ABSTRACT Data mining for processing experimental data in high energy and nuclear physics led to many multiparametric problems, two of them are considered: (i) hypothesis testing and classification approaches based on artificial neural networks and boosted decision trees (ii) clustering of large amounts of data by so-called growing neural gas. Some examples from the practice of the Joint Institute for Nuclear Research are given to show how to prepare data to deal with those approaches.

APA, Harvard, Vancouver, ISO, and other styles

23

McSherry, David. "Dynamic and static approaches to clinical data mining." Artificial Intelligence in Medicine 16, no. 1 (May 1999): 97–115. http://dx.doi.org/10.1016/s0933-3657(98)00066-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Tomar, Divya, and Sonali Agarwal. "A survey on Data Mining approaches for Healthcare." International Journal of Bio-Science and Bio-Technology 5, no. 5 (October 31, 2013): 241–66. http://dx.doi.org/10.14257/ijbsbt.2013.5.5.25.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Immel, Françoise, and Frédéric Marin. "Data Mining Approaches to Identify Biomineralization Related Sequences." Key Engineering Materials 672 (January 2016): 191–214. http://dx.doi.org/10.4028/www.scientific.net/kem.672.191.

Full text

Abstract:

Proteomics is an efficient high throughput technique developed to identify proteins from a crude extract using sequence homology. Advances in Next Generation Sequencing (NGS) have led to increase knowledge of several non-model species. In the field of calcium carbonate biomineralization, the paucity of available sequences (such as the ones of mollusc shells) is still a bottleneck in most proteomic studies. Indeed, this technique needs proteins databases to find homology. The aim of this study was to perform different data mining approaches in order to identify novel shell proteins. To this end, we disposed of several publicly non-model molluscs databases. Previously identified molluscan shell matrix sequences were used as keyword to query annotated databases. BLAST tools and KASS program (KEGG Automatic Annotation Server) were developed to analyse other non-annotated databases. Our results suggest that the efficiency of these methods depends on the quality of the shared data. Finally, an in-house shell matrix protein database has been generated.

APA, Harvard, Vancouver, ISO, and other styles

26

Ekins, Sean, Jun Shimada, and Cheng Chang. "Application of data mining approaches to drug delivery." Advanced Drug Delivery Reviews 58, no. 12-13 (November 2006): 1409–30. http://dx.doi.org/10.1016/j.addr.2006.09.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Mihai, Dana. "New approaches to processing GIS Data using Artificial Neural Networks models." Annals of the University of Craiova - Mathematics and Computer Science Series 48, no. 1 (June 30, 2021): 358–73. http://dx.doi.org/10.52846/ami.v48i1.1551.

Full text

Abstract:

Spatial data mining is a special type of data mining. The main difference between data mining and spatial data mining is that in spatial data mining tasks we use not only non-spatial attributes but also spatial attributes. Spatial data mining techniques have strong relationship with GIS (Geographical Information System) and are widely used in GIS for inferring association among spatial attributes, clustering and classifying information with respect to spatial attributes. In this paper we use the statistical package Weka on two models, which consist of two parcels plans from the Olt area of Romania. In our experimentation, we compare the results of the vector models depending on the values of the training datasets. Using these models with GIS data from the domain of Cadaster we analyze the performance of the Artificial Neural Networks in context of spatial data mining.

APA, Harvard, Vancouver, ISO, and other styles

28

Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

Full text

Abstract:

Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

APA, Harvard, Vancouver, ISO, and other styles

29

Shah, Shreya Sharadkumar. "Opinion Mining For Text Data: An Overview." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4301–11. http://dx.doi.org/10.22214/ijraset.2022.44902.

Full text

Abstract:

Abstract: Nowadays there is huge growth in data People post their views and opinions through the web on different apps, blogs, articles, etc. Customers post their reviews on shopping sites about the product or service. So, it becomes beneficial for companies, manufactures, business owners and sellers to understand customers, product users or buyers but due to huge data/feedbacks or posted opinions manually analyzing text data, is impossible to do. So, opinion mining is very important so as to analyze all the data and know the sentiments from that data without much human effort and in less time huge data can be analyzed. Many researches have made the base in this field of opinion mining. Here opinion mining will be discussed starting with what is opinion mining, how opinion mining is performed, levels, types and approaches for opinion mining, and applications. Also, methods for Text Preprocessing, Feature Extraction, Evaluation and Classification Approaches that are Machine Learning approaches and Lexicon Based approaches also, various opinion mining methods such as Support Vector machines (SVM), Neural Network, Naïve Bayes, Bayesian Network, Maximum Entropy, Corpus and Dictionary based methods are discussed here.

APA, Harvard, Vancouver, ISO, and other styles

30

Zia, Amjad, Muzzamil Aziz, Ioana Popa, Sabih Ahmed Khan, Amirreza Fazely Hamedani, and Abdul R. Asif. "Artificial Intelligence-Based Medical Data Mining." Journal of Personalized Medicine 12, no. 9 (August 24, 2022): 1359. http://dx.doi.org/10.3390/jpm12091359.

Full text

Abstract:

Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

APA, Harvard, Vancouver, ISO, and other styles

31

Salih Ageed, Zainab, Subhi R. M. Zeebaree, Mohammed Mohammed Sadeeq, Shakir Fattah Kak, Hazha Saeed Yahia, Mayyadah R. Mahmood, and Ibrahim Mahmood Ibrahim. "Comprehensive Survey of Big Data Mining Approaches in Cloud Systems." Qubahan Academic Journal 1, no. 2 (April 14, 2021): 29–38. http://dx.doi.org/10.48161/qaj.v1n2a46.

Full text

Abstract:

Cloud computing, data mining, and big online data are discussed in this paper as hybridization possibilities. The method of analyzing and visualizing vast volumes of data is known as the visualization of data mining. The effect of computing conventions and algorithms on detailed storage and data communication requirements has been studied. When researching these approaches to data storage in big data, the data analytical viewpoint is often explored. These terminology and aspects have been used to address methodological development as well as problem statements. This will assist in the investigation of computational capacity as well as new knowledge in this area. The patterns of using big data were compared in about fifteen articles. In this paper, we research Big Data Mining Approaches in Cloud Systems and address cloud-compatible problems and computing techniques to promote Big Data Mining in Cloud Systems.

APA, Harvard, Vancouver, ISO, and other styles

32

Patnaik, Debprakash, Manish Marwah, Ratnesh K. Sharma, and Naren Ramakrishnan. "Temporal data mining approaches for sustainable chiller management in data centers." ACM Transactions on Intelligent Systems and Technology 2, no. 4 (July 2011): 1–29. http://dx.doi.org/10.1145/1989734.1989738.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Grzymala-Busse, Jerzy W., Jerzy Stefanowski, and Szymon Wilk. "A Comparison of Two Approaches to Data Mining from Imbalanced Data." Journal of Intelligent Manufacturing 16, no. 6 (December 2005): 565–73. http://dx.doi.org/10.1007/s10845-005-4362-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Padhy, Neelamadhab, and M. Kannan. "State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm." International Journal of Computer Applications 64, no. 16 (February 15, 2013): 29–39. http://dx.doi.org/10.5120/10719-5485.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Liu, Feng Hua. "Research on the Data Model and the Approaches to Data Mining in the Semi-Structured Data." Applied Mechanics and Materials 513-517 (February 2014): 663–66. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.663.

Full text

Abstract:

As an important form of Internet data, semi-structured data in data mining is an important fist conditions. And the data mining was designed to find and extract large database in the implied information of value. This paper first introduced the half structured data concept characteristic, based on the data from each of the half structural said, the data model two half-and-half structured data model are introduced, finally summarizes semi-structured data model and the relationship between the data model before difference [1].

APA, Harvard, Vancouver, ISO, and other styles

36

Rajalakshmi, M., T. Purusothaman, and S. Pratheeba. "Collusion-Free Privacy Preserving Data Mining." International Journal of Intelligent Information Technologies 6, no. 4 (October 2010): 30–45. http://dx.doi.org/10.4018/jiit.2010100103.

Full text

Abstract:

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

APA, Harvard, Vancouver, ISO, and other styles

37

Mehra, Chitra, and Rashmi Agrawal. "Educational data mining approaches, challenges and goals: A review." JIMS8I - International Journal of Information Communication and Computing Technology 8, no. 2 (2020): 442–47. http://dx.doi.org/10.5958/2347-7202.2020.00008.0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Kavitha, D., B. V. Manikyala Rao, and V. Kishore Babu. "A Survey on Assorted Approaches to Graph Data Mining." International Journal of Computer Applications 14, no. 1 (January 12, 2011): 43–46. http://dx.doi.org/10.5120/1806-2294.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Najadat, Hassan. "Data Mining Classification Approaches for Malicious Executable File Detection." International Journal of Cyber-Security and Digital Forensics 7, no. 3 (2018): 238–42. http://dx.doi.org/10.17781/p002422.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Wu, Desheng. "Demonstration of data mining approaches in credit risk evaluation." International Journal of Risk Assessment and Management 9, no. 1/2 (2008): 15. http://dx.doi.org/10.1504/ijram.2008.019310.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Wu, Fang-Xiang, Min Li, Jishou Ruan, and Feng Luo. "Systems Biology Approaches to Mining High Throughput Biological Data." BioMed Research International 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/504362.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Ng, Keng-Hoong, and Kok-Chin Khor. "StockProF: a stock profiling framework using data mining approaches." Information Systems and e-Business Management 15, no. 1 (March 3, 2016): 139–58. http://dx.doi.org/10.1007/s10257-016-0313-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Khan, Dost Muhammad, Tariq Aziz Rao, and Faisal Shahzad. "The Classification of Customers’ Sentiment using Data Mining Approaches." Global Social Sciences Review IV, no. IV (December 30, 2019): 146–56. http://dx.doi.org/10.31703/gssr.2019(iv-iv).19.

Full text

Abstract:

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machine-learning algorithms for classification of customers’ sentiments. The proposed framework also tested to evaluate the system’s performance. Our proposed methodology will be helpful for researchers, service providers, and decisionmakers.

APA, Harvard, Vancouver, ISO, and other styles

44

Cova, Tânia F. G. G., Daniel J. Bento, and Sandra C. C. Nunes. "Computational Approaches in Theranostics: Mining and Predicting Cancer Data." Pharmaceutics 11, no. 3 (March 13, 2019): 119. http://dx.doi.org/10.3390/pharmaceutics11030119.

Full text

Abstract:

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

APA, Harvard, Vancouver, ISO, and other styles

45

Lee, Ickjai, Guochen Cai, and Kyungmi Lee. "Exploration of geo-tagged photos through data mining approaches." Expert Systems with Applications 41, no. 2 (February 2014): 397–405. http://dx.doi.org/10.1016/j.eswa.2013.07.065.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Chubukova, Ponomarenko, and Nedbailo. "Using data mining to process business data." Problems of Innovation and Investment Development, no. 23 (April 10, 2020): 71–77. http://dx.doi.org/10.33813/2224-1213.23.2020.8.

Full text

Abstract:

The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.

APA, Harvard, Vancouver, ISO, and other styles

47

ÖZYİĞİT, Hüseyin. "CURRENT APPROACHES TO ACCOUNTING: TEXT MINING." Muhasebe ve Vergi Uygulamaları Dergisi 15, no. 3 (November 1, 2022): 637–63. http://dx.doi.org/10.29067/muvu.1104525.

Full text

Abstract:

Text mining; It is a multidisciplinary branch of knowledge that includes concepts and techniques from different fields such as information sciences, linguistics, computer science and data science. With the transition of organizations from paper data to electronic documents and digital records, the rapid digitization of business processes has increased the interest in text mining. Due to the growing data in the field of accounting, text mining technology has become an important research topic for this field. The aim of this study; In the field of accounting, by giving information on the use of text mining, it is to reveal the effect of this technology on organizations and individuals in the future in a concise way. As a result, the use of text mining technology in the field of accounting; accounting automation, audit automation, tax automation and business consultancy automation. In addition, it is predicted that text mining combined with artificial intelligence and machine learning approaches will offer significant opportunities to organizations and accounting professionals, as it automates processes much more.

APA, Harvard, Vancouver, ISO, and other styles

48

Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (September 2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.

Full text

Abstract:

The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.

APA, Harvard, Vancouver, ISO, and other styles

49

Mansour, ManaL, and Manal Abdullah. "Mining Techniques for Streaming Data." International Journal of Data Mining & Knowledge Management Process 12, no. 2 (March 31, 2022): 1–14. http://dx.doi.org/10.5121/ijdkp.2022.12201.

Full text

Abstract:

The huge explosion in using real time technology leads to infinite flow of data which known as data streams. The characteristics of streaming data require different techniques for processing due its volume, velocity and volatility, beside issues related to the limited storage capabilities. Hence, this research highlights the significant aspects to consider when building a framework for mining data streams. It reviews the methods for data stream summarizing and creating synopsis, and the approaches of processing these data synopses. The goal is to present a model for mining the streaming data which describes the main phases of data stream manipulation.

APA, Harvard, Vancouver, ISO, and other styles

50

Komitova, Rumena, Dominik Raabe, Robert Rein, and Daniel Memmert. "Time Series Data Mining for Sport Data: a Review." International Journal of Computer Science in Sport 21, no. 2 (December 1, 2022): 17–31. http://dx.doi.org/10.2478/ijcss-2022-0008.

Full text

Abstract:

Abstract Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Data Mining Approaches'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles