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Journal articles on the topic 'Data Mining Techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 April 2024

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1

Shah Neha K, Shah Neha K. "Introduction of Data mining and an Analysis of Data mining Techniques." Indian Journal of Applied Research 3, no. 5 (October 1, 2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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Sherdiwala, Kainaz Bomi. "Data Mining Techniques in Stock Market." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 327–29. http://dx.doi.org/10.15373/2249555x/august2014/82.

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3

S, Gowtham, and Karuppusamy S. "Review of Data Mining Classification Techniques." Bonfring International Journal of Software Engineering and Soft Computing 9, no. 2 (April 30, 2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.

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4

M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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SAKURAI, Shigeaki. "Data Mining Techniques." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 21, no. 3 (2009): 348–57. http://dx.doi.org/10.3156/jsoft.21.3_348.

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6

Hegland, Markus. "Data mining techniques." Acta Numerica 10 (May 2001): 313–55. http://dx.doi.org/10.1017/s0962492901000058.

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Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role.

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7

Han, Jiawei. "Data mining techniques." ACM SIGMOD Record 25, no. 2 (June 1996): 545. http://dx.doi.org/10.1145/235968.280351.

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D, Premalatha, Niveditha N, Poornima Vasanth V, Priyanka G. N, and Niveditha K B. "CANCER PREDICTION SYSTEM USING DATA MINING TECHNIQUES." International Journal of Current Engineering and Scientific Research 6, no. 6 (June 2019): 165–68. http://dx.doi.org/10.21276/ijcesr.2019.6.6.28.

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Delima, Allemar Jhone P. "Predicting Scholarship Grants Using Data Mining Techniques." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 513–19. http://dx.doi.org/10.18178/ijmlc.2019.9.4.834.

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Lakshmi R, Aishwarya, and SashiKumar D R. "A Survey of Data Mining Techniques for Internet." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (January 30, 2017): 22–35. http://dx.doi.org/10.23956/ijarcsse/v7i1/0125.

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Bhojani, Shital Hitesh. "Geospatial Data Mining Techniques: Knowledge Discovery in Agricultural." Indian Journal of Applied Research 3, no. 1 (October 1, 2011): 22–24. http://dx.doi.org/10.15373/2249555x/jan2013/10.

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Srikanth, V., and Ashish Chaturvedi. "Analysis on Data Mining Techniques in Online Shopping." Journal of Advances and Scholarly Researches in Allied Education 15, no. 3 (May 1, 2018): 45–48. http://dx.doi.org/10.29070/15/56763.

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M. S. Chaudhari, and Dr N. K. Choudhari. "Review of Data Mining Techniques in Environmental System." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 04, no. 05 (October 2016): 01–04. http://dx.doi.org/10.9756/sijcsea/v4i5/04050100201.

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Singh, Sakshi, Harsh Mittal, and Archana Purwar. "Prediction of Investment Patterns Using Data Mining Techniques." International Journal of Computer and Communication Engineering 3, no. 2 (2014): 145–48. http://dx.doi.org/10.7763/ijcce.2014.v3.309.

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15

Dubey, Aashaykumar, Saurabh Kamath, and Dhruv Kanakia. "Learning Data Mining Techniques." International Journal of Computer Applications 136, no. 11 (February 17, 2016): 5–8. http://dx.doi.org/10.5120/ijca2016908201.

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C, Christys, and Arivalagan S. "Text Mining Techniques in Data Mining – Review." International Journal of Data Mining Techniques and Applications 7, no. 1 (March 15, 2018): 181–85. http://dx.doi.org/10.20894/ijdmta.102.007.001.029.

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Sowmiya,, T., M. Gopi,, M. New Begin,, and L. Thomas Robinson. "Optimization of Lung Cancer using Modern Data Mining Techniques." International Journal of Engineering Research 3, no. 5 (May 1, 2014): 309–14. http://dx.doi.org/10.17950/ijer/v3s5/503.

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Reyaz, Misba, and Gagan Dhawan. "Various Data Mining Techniques for Diabetes Prognosis: A Review." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 305–10. http://dx.doi.org/10.31142/ijtsrd12927.

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19

B, Valliappan, and Xavier Xavier. "A Study on Web Data Mining – Tools and Techniques." International Journal of Research Publication and Reviews 5, no. 1 (January 24, 2024): 4139–43. http://dx.doi.org/10.55248/gengpi.5.0124.0316.

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20

Elango, P., K. Kuppusamy, and N. Prabhu. "Data Replication Using Data Mining Techniques." Asian Journal of Computer Science and Technology 8, S1 (April 22, 2021): 107–9. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1939.

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Database Replication is the successive electronic duplicating of information from a database in one PC or server to a database in another with the goal that all clients share a similar dimension of data. The outcome is a conveyed database in which clients can get to information significant to their assignments without meddling with crafted by others. Anyway information replication is an entrancing theme for both hypothesis and practice. On the hypothetical side, numerous solid outcomes requirement what should be possible as far as consistency: e.g., the difficulty of achieving agreement in offbeat frameworks the blocking idea of CAP hypothesis, and the requirement for picking an appropriate rightness foundation among the numerous conceivable. On the pragmatic side, information replication assumes a key job in a wide scope of settings like storing, back-up, high accessibility, wide territory content dissemination, expanding versatility, parallel preparing, and so forth. Finding a replication arrangement that is reasonable in whatever number such settings as could reasonably be expected remains an open test.

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Sharma, Pragati, and Dr Sanjiv Sharma. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." International Journal of Engineering Technologies and Management Research 5, no. 2 (May 1, 2020): 166–77. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.641.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

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22

Obenshain, Mary K. "Application of Data Mining Techniques to Healthcare Data." Infection Control & Hospital Epidemiology 25, no. 8 (August 2004): 690–95. http://dx.doi.org/10.1086/502460.

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AbstractA high-level introduction to data mining as it relates to surveillance of healthcare data is presented. Data mining is compared with traditional statistics, some advantages of automated data systems are identified, and some data mining strategies and algorithms are described. A concrete example illustrates steps involved in the data mining process, and three successful data mining applications in the healthcare arena are described.

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Rahman, Nayem. "Data Mining Techniques and Applications." International Journal of Strategic Information Technology and Applications 9, no. 1 (January 2018): 78–97. http://dx.doi.org/10.4018/ijsita.2018010104.

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Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.

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24

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.

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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.

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25

Алисултанова, Э. Д., Л. К. Хаджиева, and З. А. Шудуева. "DATA MINING TECHNIQUES IN EDUCATION." Вестник ГГНТУ. Гуманитарные и социально-экономические науки, no. 2(28) (August 26, 2022): 47–54. http://dx.doi.org/10.34708/gstou.2022.16.83.006.

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В данной статье проводится обзор и обосновывается актуальность применения методов интеллектуального анализа данных в образовании. Изложены особенности и основные методы, применяемые для анализа данных в исследуемой области. Описанные методы наиболее актуальны и употребимы в системах поддержки принятия решений. На современном этапе развития информационного объема данных рынок труда требует новых инструментов и методов для поддержки больших хранилищ данных для оптимальной выборки и получения необходимой информации. Интеллектуальный анализ данных (Data mining) направлен на выявление и обработку информации из большого массива, требуемой для принятия решений в определенных сферах деятельности человека. На сегодняшний день области применения Data mining включают такие сферы, как бизнес, образование, сельское хозяйство, медицину и другие. В данном аспекте использование искусственного интеллекта, машинного обучения и методов визуализации данных имеют колоссальное значение для цифровой экономики РФ. This article reviews and substantiates the relevance of the application of data mining methods in education. The features and main methods used to analyze data in the study area are outlined. The described methods are the most relevant and usable in decision support systems. At the present stage of development of the information volume of data, the labor market requires new tools and methods to support large data warehouses for optimal sampling and obtaining the necessary information. Data mining is aimed at identifying and processing information from a large array required for decision making in certain areas of human activity. To date, the areas of application of Data mining include such areas as business, education, agriculture, medicine and others. In this aspect, the use of artificial intelligence, machine learning and data visualization methods are of great importance for the digital economy of the Russian Federation.

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Satish Babu, J., M. Niveditha, V. Bhavya, and K. Gowthami. "Data mining techniques for herbs." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 406. http://dx.doi.org/10.14419/ijet.v7i1.1.9943.

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The most important source of ingredients in the discovery of new drugs are Natural products. Moreover Nagoya protocol is most commonly used in selection of herbs based on similar efficiency, Later scientists have voiced their concern on protocol also proved it as less effective therefore, this project uses data mining classification approaches, novel targeted Selection which makes use of MED - LINE(Medical Literature Analysis and Retrieval system online) database that consists of biomedical information to identify herbs of same efficacy .Neural network technique among all classification techniques is inspired by biological nervous system. AS neural network is successful on wide array of noisy object selection of herbs is done effectively. SOM (self-organizing map) is most popular Neural Network provides a topology preserving mapping from the high dimensional space to map units. The main objective of this project is to survey on various data mining methods and their techniques and to conclude the suitable algorithm.

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27

H, H. Kaleemullah, and A. S. Tincky Shalinee. "Data Mining Techniques and Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 1139–42. http://dx.doi.org/10.22214/ijraset.2022.48009.

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Abstract: In recent days internet is considered as the main supply for searching the information and collecting data . The extraction of the data from the web offers several query results. Machine-controlled tools are needed through queries from the amount of pages by using the internet to spot the connected info. Data mining method is taken into account an efficient method of extracting the relevant information from databases. This method is employed for the pattern identification. Data mining could be a method that finds helpful patterns from great amount of knowledge. The paper discusses few of the information mining techniques, algorithms and a few of the organizations that have adapted data processing technology to enhance their businesses and located glorious results

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H N, Punya, and Dr Bharathi. "Using Data Mining Techniques for Field Data." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 2008–10. http://dx.doi.org/10.22214/ijraset.2023.55065.

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Abstract: The population of India is continuously increasing and to meet the food necessities of this growing population, agricultural yield should be boosted. Knowledge discovered from raw data is useful for many purposes. This paper aims to analyse the field data using data mining algorithms and to find useful information from the results of these techniques which would help to improve the agricultural yield. Various mining algorithms applied on agricultural data were studied. Data mining techniques applied in this paper include clustering algorithms- K- means, DBSCAN, EM, the results of these algorithms are analysed.

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Mirarchi, Domenico, Giovanni Canino, Patrizia Vizza, Pierangelo Veltri, Salvatore Cuomo, Claudio Petrolo, and Giuseppe Chiarella. "Data mining techniques for vestibular data classification." International Journal of Internet Technology and Secured Transactions 7, no. 1 (2017): 51. http://dx.doi.org/10.1504/ijitst.2017.085734.

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Petrolo, Claudio, Salvatore Cuomo, Pierangelo Veltri, Patrizia Vizza, Giovanni Canino, Domenico Mirarchi, and Giuseppe Chiarella. "Data mining techniques for vestibular data classification." International Journal of Internet Technology and Secured Transactions 7, no. 1 (2017): 51. http://dx.doi.org/10.1504/ijitst.2017.10006656.

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Mabu, Audu Musa, Rajesh Prasad, and Raghav Yadav. "Mining gene expression data using data mining techniques: A critical review." Journal of Information and Optimization Sciences 41, no. 3 (October 31, 2019): 723–42. http://dx.doi.org/10.1080/02522667.2018.1555311.

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32

Rahman, Nayem. "Data Mining Problems Classification and Techniques." International Journal of Big Data and Analytics in Healthcare 3, no. 1 (January 2018): 38–57. http://dx.doi.org/10.4018/ijbdah.2018010104.

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Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

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Saxena, Aditya, Megha Jain, and Prashant Shrivastava. "Data Mining Techniques Based Diabetes Prediction." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (April 10, 2021): 29–35. http://dx.doi.org/10.35940/ijainn.b1012.041221.

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Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world's greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.

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Saxena, Aditya, Megha Jain, and Prashant Shrivastava. "Data Mining Techniques Based Diabetes Prediction." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (April 10, 2021): 29–35. http://dx.doi.org/10.54105/ijainn.b1012.041221.

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Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world’s greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.

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35

Hosseinkhani, Javad, Suhaimi Ibrahim, Suriayati Chuprat, and Javid Hosseinkhani Naniz. "Web Crime Mining by Means of Data Mining Techniques." Research Journal of Applied Sciences, Engineering and Technology 7, no. 10 (March 15, 2014): 2027–32. http://dx.doi.org/10.19026/rjaset.7.495.

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Sanmiquel, Lluís, Josep M. Rossell, and Carla Vintró. "Study of Spanish mining accidents using data mining techniques." Safety Science 75 (June 2015): 49–55. http://dx.doi.org/10.1016/j.ssci.2015.01.016.

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Raiyani, Ronak S., Dr Bankim Radadiya, and Dr Satish Thumar. "Analyzing, Developing and Implementing Data Mining Techniques on Databases, Web Contents and Textual Data." Paripex - Indian Journal Of Research 2, no. 3 (January 15, 2012): 48–50. http://dx.doi.org/10.15373/22501991/mar2013/18.

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Joseph, Jyothis, and Ratheesh T K. "Rainfall Prediction using Data Mining Techniques." International Journal of Computer Applications 83, no. 8 (December 18, 2013): 11–15. http://dx.doi.org/10.5120/14467-2750.

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Yu, Wenwei. "Data Mining Techniques in Medical Informatics." Open Medical Informatics Journal 4, no. 1 (August 31, 2010): 21–22. http://dx.doi.org/10.2174/1874431101004010021.

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Acharya, U. Rajendra, and Wenwei Yu. "Data Mining Techniques in Medical Informatics." Open Medical Informatics Journal 4, no. 1 (May 28, 2010): 21–22. http://dx.doi.org/10.2174/1874431101004020021.

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Критська, Я. О., T. O. Білобородова, and І. С. Скарга-Бандурова. "Data mining techniques for IoT analytics." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 53–62. http://dx.doi.org/10.33216/1998-7927-2019-253-5-53-62.

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Data mining (DM) is one of the most valuable technologies enable to identify unknown patterns and make Internet of Things (IoT) smarter. The current survey focuses on IoT data and knowledge discovery processes for IoT. In this paper, we present a systematic review of various DM models and discuss the DM techniques applicable to different IoT data. Some data specific features were analyzed, and algorithms for knowledge discovery in IoT data were considered.Challenges and opportunities for mining multimodal, heterogeneous, noisy, incomplete, unbalanced and biased data as well as massive datasets in IoT are also discussed.

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Rony, Md Sumon, Sagor Chandra Bakchy, and Hadisur Rahman. "Crime Detection using Data Mining Techniques." Computer Science & Engineering: An International Journal 10, no. 5 (October 30, 2020): 1–5. http://dx.doi.org/10.5121/cseij.2020.10501.

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As crime rates keep spiraling each day, new challenges are faced by law enforcement agencies. They have to keep their on the lookout for any signs criminal activity. The law enforcement agencies should therefore be able to predict such increase or decrees or trends in crime. Such as theft, Killing. Crime that may occur in a particular area in a particular month, year, any timespan. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence, statistical. Many algorithms for data mining approach to help detect the crimes patterns. Data Collection, Data Preprocessing Phase, Data Filtering, Linier Regression. Wekasoft are used for collection of data analyzing. Visualization finally get results. The advantage of using this tool is that clustering will be performed automatically.

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Kaur, Shaganpreet, and Chinu . "Comparative Analysis of Data Mining Techniques." International Journal of Computer Sciences and Engineering 6, no. 4 (April 30, 2018): 301–4. http://dx.doi.org/10.26438/ijcse/v6i4.301304.

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Patel, Disha, Bhavesh Tanwala, and Pranay Patel. "Breast Cancer Using Data Mining Techniques." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 1531–36. http://dx.doi.org/10.26438/ijcse/v6i7.15311536.

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Punjani, Dipti N., and Kishor H. Atkotiya. "Data Mining Techniques in Biological Research." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 339–43. http://dx.doi.org/10.26438/ijcse/v7i4.339343.

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Alharthi, Manal Mansour, and Manal Abdulaziz Abdullah. "Mining Data Streams using Clustering Techniques." International Journal of Computer Applications 184, no. 7 (April 20, 2022): 9–15. http://dx.doi.org/10.5120/ijca2022922027.

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Nennuri, Rajashekar, M. Geetha Yadav, M. Samhitha, S. Sandeep Kumar, and G. Roshini. "Plagiarism Detection through Data Mining Techniques." Journal of Physics: Conference Series 1979, no. 1 (August 1, 2021): 012070. http://dx.doi.org/10.1088/1742-6596/1979/1/012070.

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Revathi, Mrs S. "Data Mining Techniques with Cloud Security." International Journal for Research in Applied Science and Engineering Technology 7, no. 11 (November 30, 2019): 468–72. http://dx.doi.org/10.22214/ijraset.2019.11076.

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Dhanapal, Dr R., S. Gayathri Subramanian, and Jobin M. Scaria. "Customer Retention using Data Mining Techniques." International Journal of Computer Applications 11, no. 5 (December 10, 2010): 32–34. http://dx.doi.org/10.5120/1576-2108.

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Jain, Sheenam, and Vijay Kumar. "Garment Categorization Using Data Mining Techniques." Symmetry 12, no. 6 (June 9, 2020): 984. http://dx.doi.org/10.3390/sym12060984.

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Abstract:

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

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