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Статті в журналах з теми "Mining Techniques and Pattern Discovery"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

J., Umarani, and S. Manikandan Dr. "PATTERN DISCOVERY TECHNIQUES IN WEB USAGE MINING." International Journal of Scientific Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332044.

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Анотація:

WWW is a very popular and interactive medium for broadcasting information today. Due to the vast, diverse and lively nature of web it advancesthe scalability, multimedia data and temporal issues respectively. The development of the web has given rise to large quantity of data that is freely available for user access.Web Usage Mining enhances the user experience while browsing web pages by using past history of web data. It also used to improve the web site navigation. Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web usage mining is

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2

Suraj, Jain1 Siddu P. Algur2 *Basavaraj A. Goudannavar 3. Prashant Bhat4. "REVIEW ON WEB MULTIMEDIA MINING AND KNOWLEDGE DISCOVERY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 3 (2018): 449–55. https://doi.org/10.5281/zenodo.1199346.

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Web Multimedia data mining (WMDM) can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. MDM is the mining of knowledge and high level multimedia information from large multimedia database system. MDM refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. To extract knowledge from multimedia database multimedia techniques are used. We compare MDM techniques with the state of the art data mining techniques involving clu

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3

Et. al., V. Aruna,. "A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1634–39. http://dx.doi.org/10.17762/turcomat.v12i2.1448.

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In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge presen

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4

Mahoto, Naeem Ahmed, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon, and Adel Sulaiman. "Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment." Sustainability 13, no. 16 (2021): 8900. http://dx.doi.org/10.3390/su13168900.

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The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transform

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5

Xu, Dalin, and Yingmei Wei. "Study on Visual Techniques of Potential Pattern Discovery for Time Series Data." MATEC Web of Conferences 232 (2018): 02049. http://dx.doi.org/10.1051/matecconf/201823202049.

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Sequential pattern mining is always a very important branch of time series data mining. The pattern mining with visual means can be used to extract the knowledge of time series data more intuitively. Based on the research content, this paper analyzes the sequence pattern mining methods in different aspects and their combination with visualization technology. We further discuss and summarize the advantages of different visualization methods in discovering the potential patterns in time series data. Different systems and models have their unique information to show the focus. Compared with the c

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6

Muley, Abhinav. "Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 3844–49. http://dx.doi.org/10.1166/jctn.2020.9046.

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With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge

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7

Babu, S. Suresh, Vahiduddin Shariff, and CH M. H. Saibaba. "Data Mining Techniques Based on Effective Pattern Discovery." International Journal of u- and e- Service, Science and Technology 9, no. 7 (2016): 197–202. http://dx.doi.org/10.14257/ijunesst.2016.9.7.20.

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8

Patel, Ketul, and Dr A. R. Patel. "Process of Web Usage Mining to find Interesting Patterns from Web Usage Data." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.

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The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to usersâ€™ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also p

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9

Ouyang, Zhiping, Lizhen Wang, and Pingping Wu. "Spatial Co-Location Pattern Discovery from Fuzzy Objects." International Journal on Artificial Intelligence Tools 26, no. 02 (2017): 1750003. http://dx.doi.org/10.1142/s0218213017500038.

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A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern min

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10

ALI, Sura I. Mohammed, and Rafid Habib BUTI. "DATA MINING IN HEALTHCARE SECTOR." MINAR International Journal of Applied Sciences and Technology 03, no. 02 (2021): 87–91. http://dx.doi.org/10.47832/2717-8234.2-3.11.

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Disease detection is one of the applications where data mining techniques achieved more accurate and useful results. The healthcare sector collects massive volumes of healthcare data that are not mine to discover hidden data for better decision-making, a field of data mining introduces more efficiently and effectively to predict different kinds of diseases. Clustering medical data into small, meaningful chunks will help in pattern discovery by allowing for the retrieval of a large number of specific data points. The difference in using clustering the medical data from traditional data mining t

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11

Sharma, Vikrant. "Relevance Feature Discovery for Text Mining." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 225–33. http://dx.doi.org/10.17762/msea.v70i1.2303.

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Due to large size words also data patterns, it is difficult to ensure the quality of relevant characteristics that are found in text documents that describe user preferences. Most widely used text mining and classification techniques now in use have embraced term-based strategies. However, polysemy and synonymy issues have affected them all. The theory that pattern-based approaches should outperform term-based ones in performance in expressing user preferences has been often held throughout the years, however text mining still struggles with how to employ large-scale patterns successfully. Thi

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12

Ashwini Brahme. "Association Rule Mining and Information Retrieval Using Stemming and Text Mining Techniques." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 622–28. https://doi.org/10.52783/jisem.v10i18s.2958.

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Heterogeneous, complex and enormous data mining plays significant role in the today’s big data scenario all over the globe. The research paper is intended toward the natural language processing, mining of textual data, and pattern discovery through association rule mining. The research is aimed towards mining of digital news of epidemic diseases and generating the hidden patterns from the corpus data. The present study also aimed towards developing knowledge discovery system for healthcare for prediction of epidemic viral diseases and their related measures which will be helpful for the health

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13

Dzyuba, Vladimir, Matthijs van Leeuwen, Siegfried Nijssen, and Luc De Raedt. "Interactive Learning of Pattern Rankings." International Journal on Artificial Intelligence Tools 23, no. 06 (2014): 1460026. http://dx.doi.org/10.1142/s0218213014600264.

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Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to

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14

Rajendra, Chouhan, Sawant Khushboo, and Harish Patidar Dr. "A Compression Based Methodology to Mine All Frequent Items." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 266–68. https://doi.org/10.31142/ijtsrd18423.

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Анотація:

Data mining is not new. People who first discovered how to start fire and that the earth is round also discovered knowledge which is the main idea of Data mining. Data Mining, also called knowledge Discovery in Database, is one of the latest research area, which has emerged in response to the Tsunami data or the flood of data, world is facing nowadays. It has taken up the challenge to develop techniques that can help humans to discover useful patterns in assive data. One such important technique is frequent pattern mining. This paper will present an compression based technique for mining frequ

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15

Singh, Pushpa, and Narendra Singh. "Role of Data Mining Techniques in Bioinformatics." International Journal of Applied Research in Bioinformatics 11, no. 1 (2021): 51–60. http://dx.doi.org/10.4018/ijarb.2021010106.

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Анотація:

Data mining offers a highly effective technique that is useful in research and development of bioinformatics. Bioinformatics consists biological information such as DNA, RNA, and protein. Data mining tasks/techniques are classification, prediction, clustering, association, outlier detection, regression, and pattern tracking. Data mining provides important correlation, hidden patterns, and knowledge from the bioinformatics data set. This paper presents the role of data mining techniques in bioinformatics application. Classification of gene and protein structure, analyzing the gene expression, a

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16

R., Rajamani*1 &. S. Saranya2. "A STUDY OF TEXT MINING METHODS, APPLICATIONS,AND TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 623–28. https://doi.org/10.5281/zenodo.829803.

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Анотація:

Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mining, it is also called knowledge discovery in text (KDT) or knowledge of intelligent text analysis. The process is driving high-quality information from not-structured to semi-structured data. Text mining

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17

Karan, Manchandia* Navdeep Khare. "IMPLEMENTATION OF STUDENT PERFORMANCE EVALUATION THROUGH SUPERVISED LEARNING USING NEURAL NETWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 3 (2017): 424–30. https://doi.org/10.5281/zenodo.438101.

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As we know that in maximum university the evaluation of the student’s performance I done manually by the faculties. This System of student performance evaluation is non-transparent and often leads to dissatisfaction among students. This project aims to solve the problem by designing a user interface which would work on supervised learning using Neural Network. Data mining techniques are widely used in educational field to find new hidden patterns from student’s data. The hidden patterns that are discovered can be used to understand the problem arise in the educational field. Data Mining (DM),

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18

Karan, Manchandia* Shweta Kondla Vasudev Lambhate. "REVIEW PAPER ON STUDENT PERFORMANCE EVALUATION THROUGH SUPERVISED LEARNING USING NEURAL NETWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 3 (2017): 435–39. https://doi.org/10.5281/zenodo.438106.

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Анотація:

As we know that in maximum university the evaluation of the student’s performance I done manually by the faculties. This System of student performance evaluation is non-transparent and often leads to dissatisfaction among students. This project aims to solve the problem by designing a user interface which would work on supervised learning using Neural Network. Data mining techniques are widely used in educational field to find new hidden patterns from student’s data. The hidden patterns that are discovered can be used to understand the problem arise in the educational field. Data Mining (DM),

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19

DONG, JING, YAJING ZHAO, and TU PENG. "A REVIEW OF DESIGN PATTERN MINING TECHNIQUES." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (2009): 823–55. http://dx.doi.org/10.1142/s021819400900443x.

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The quality of a software system highly depends on its architectural design. High quality software systems typically apply expert design experience which has been captured as design patterns. As demonstrated solutions to recurring problems, design patterns help to reuse expert experience in software system design. They have been extensively applied in the industry. Mining the instances of design patterns from the source code of software systems can assist in the understanding of the systems and the process of re-engineering them. More importantly, it also helps to trace back to the original de

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20

N., Thinaharan, Chitradevi B., Malathi P., and Kalpana K. "A LITERATURE SURVEY ON DATA MINING TECHNIQUES AND CONCEPTS." International Journal of Engineering Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332042.

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Анотація:

Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. Data mining is the process of analyzing data from different views and summarizing it into useful data. “Data mining, also popularly referred to as knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large data

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21

Karan, Manchandia* Navdeep Khare Mohit Agrawal. "WEKA AS A DATA MINING TOOL TO ANALYZE STUDENTS' ACADEMIC PERFORMANCES USING NAÏVE BAYES CLASSIFIER- A SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 3 (2017): 431–34. https://doi.org/10.5281/zenodo.438104.

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Анотація:

In Indian Education System, the student performance evaluation is done by faculty manually. This System of student performance evaluation is non-transparent and often leads to dissatisfaction of student. This project aims to solve this problem by designing a user interface which would work on learning using Naïve Bayes Classifier.In evaluating the marks of students by faculty, many times there is partiality done by faculty while giving marks to the students. Therefore to cease this problem the concept of data mining is introduced. Data mining techniques are widely used in educational field to

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22

Rahman, Nayem. "A Taxonomy of Data Mining Problems." International Journal of Business Analytics 5, no. 2 (2018): 73–86. http://dx.doi.org/10.4018/ijban.2018040105.

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Анотація:

Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data mining problems. But there is no single source that identifies which techniques solve what problems and how, the advantages and limitations, and real-life use-cases. Lately, identifying data mining techniques and corresponding problems that they solve has drawn significant attention. In this paper, the author describes the progress made in developing data mining techni

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Kamalpreet, Kaur, and Kaur Kiranbir. "Modified Associative Algorithm to Determine Frequent Pattern from Student Dataset." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2449–52. https://doi.org/10.35940/ijeat.D8321.049420.

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Анотація:

The phenomenal advances in Students produces huge amount of data like MOOC data and high throughput information that makes Electronic Student records (ESRs) expensive and complex. For the analysis of such a huge amount of data, AI and data mining techniques have been utilized along with Student services. Today, Data mining is utilized to detect performances using various informational datasets along with machine learning algorithms. There are many techniques available which are utilized for diagnosis of student performance like FP growth, Apriori and Associative algorithm etc. These techniques

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24

Melati, IGA Sri, Linawati Linawati, and I. A. D. Giriantari. "Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa." Majalah Ilmiah Teknologi Elektro 17, no. 3 (2018): 325. http://dx.doi.org/10.24843/mite.2018.v17i03.p04.

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Admission of new students to an educational institution such as STMIK STIKOM Bali was an activity which is routinely implemented every new academic year. The registration of new student candidates was always increasing from year to year, but not all prospective students continued registration step of a number of prospective students who had passed. It would be too late to take action if a new student enrolled very little. By not knowing the number of registration students, institution cannot measure the time and the number of new admissions target which had been achieved. In this case th

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25

Chinchuluun, Altannar, Petros Xanthopoulos, Vera Tomaino, and P. M. Pardalos. "Data Mining Techniques in Agricultural and Environmental Sciences." International Journal of Agricultural and Environmental Information Systems 1, no. 1 (2010): 26–40. http://dx.doi.org/10.4018/jaeis.2010101302.

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Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is importan

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Degadwala, Dr Sheshang, and Dhairya Vyas. "Data Mining Approch for Amino Acid Sequence Classification." International Journal of New Practices in Management and Engineering 10, no. 04 (2021): 01–08. http://dx.doi.org/10.17762/ijnpme.v10i04.124.

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Computerized applications are employed all around the world, an enormous amount of data is collected. The essential information contained in large amounts of data is attracting scholars from a variety of disciplines to examine how to extract the hidden knowledge inside them. The technique of obtaining or mining usable and valuable knowledge from enormous amounts of data is known as data mining. Text mining, picture mining, sequential pattern mining, web mining, and so on are all examples of data mining fields. Sequencing mining is one of the most important technologies in this field, as it aid

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Mariñelarena-Dondena, Luciana, Marcelo Luis Errecalde, and Alejandro Castro Solano. "Extracción de conocimiento con técnicas de minería de textos aplicadas a la psicología." Revista Argentina de Ciencias del Comportamiento 9, no. 2 (2017): 65–76. http://dx.doi.org/10.32348/1852.4206.v9.n2.12701.

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The knowledge discovery in databases (KDD) is concerned with the non-trivial process of making sense of data. Data mining is only a step in the KDD process that consists in pattern recognition using statistics and machine learning techniques. This literature review focuses on how text mining techniques can be applied in Psychology. In this context, the two main purposes of text mining techniques will be introduced: description and prediction. Finally, this paper highlights the use of text mining techniques as a psychological assessment tool, which differs from the use of standard questionnaire

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28

Meddah, Ishak H. A., and Nour El Houda REMIL. "Parallel and Distributed Pattern Mining." International Journal of Rough Sets and Data Analysis 6, no. 3 (2019): 1–17. http://dx.doi.org/10.4018/ijrsda.2019070101.

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The treatment of large data is difficult and it looks like the arrival of the framework MapReduce is a solution of this problem. This framework can be used to analyze and process vast amounts of data. This happens by distributing the computational work across a cluster of virtual servers running in a cloud or a large set of machines. Process mining provides an important bridge between data mining and business process analysis. Its techniques allow for extracting information from event logs. Generally, there are two steps in process mining, correlation definition or discovery and the inference

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Ykhlef, Mourad, and Hebah ElGibreen. "Mining Pharmacy Database Using Evolutionary Genetic Algorithm." International Journal of Electronics and Telecommunications 56, no. 4 (2010): 427–32. http://dx.doi.org/10.2478/v10177-010-0058-4.

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Анотація:

Mining Pharmacy Database Using Evolutionary Genetic AlgorithmMedication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data

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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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Анотація:

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

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Vats, Deepak, and Avinash Sharma. "Analysis and Comparison of Various Web Mining Techniques." Journal of Computational and Theoretical Nanoscience 16, no. 10 (2019): 4125–34. http://dx.doi.org/10.1166/jctn.2019.8491.

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Анотація:

The reason at the back of data overloading dilemma faced by internet users on internet includes: excessive web information and billions of users around worldwide. Because of this, providing the internet users with more intended data is a challenging task in web applications. The lots of information available on internet are a fertile field for applying data mining techniques. This is what we call Web Mining (WM). The research in WM deals with research from many fields like database, Artificial Intelligence (machine learning [supervised, semi supervised, unsupervised and reinforcement], neural

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Zahraa Raji Alzobaidy. "Data mining application in education." World Journal of Advanced Engineering Technology and Sciences 14, no. 2 (2025): 173–82. https://doi.org/10.30574/wjaets.2025.14.2.0070.

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Анотація:

In the digital era we live, the education produces huge amount of data from different resources like learning management system, electronic tests, students records. Educational Data Mining is the discovery of hidden useful knowledge and pattern in educational data concerned with studying and analyzing data from academic database, which is very large datasets to reveal main concept and relationships that improves education process and making more precise decisions. In this paper we explored common data mining techniques and their application in educational context, were automatically used to ex

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Mr., Nilesh Kumar Dokania*1 &. Ms. Navneet Kaur2. "COMPARATIVE STUDY OF VARIOUS TECHNIQUES IN DATA MINING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 202–9. https://doi.org/10.5281/zenodo.1241440.

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Data mining (knowledge discovery from data) may be viewed as the extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns and models from observed data or a method used for analytical process designed to explore data. We know Data mining as knowledge discovery. Basically Extraction or “MINING” means knowledge from large amount of data. We use Data mining due to the explosive growth of data i.e. from terabytes to petabytes. We are drowning in data, but starving for knowledge! Alternative names of Data mining are: Data archeology, Data dre

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M.Rani, R., and M. Pushpalatha. "Discovery of Knowledge Using Association Rules in Wireless Sensor Epocs-a Survey." International Journal of Engineering & Technology 7, no. 4.10 (2018): 436. http://dx.doi.org/10.14419/ijet.v7i4.10.21035.

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Data mining and knowledge discovery in huge data streams have recently involved in more applications used for decision making. Currently in wireless sensor networks, various mining techniques are used to discover knowledge on sensor data. Applying mining algorithm in wireless sensor data faces many challenges such as continuous arrival of sensor data, fast and huge data arrival, changes of mining results over time, online mining, data transformation, changing network topology, resource constraints and have emerged into various research problems. In Wireless Sensor Database, this paper presents

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Nata, Gusti Ngurah Mega, Steven Anthony, and Putu Pande Yudiastra. "Knowledge Discovery And Virtual Tour To Support Tourism Promotion." IAIC Transactions on Sustainable Digital Innovation (ITSDI) 2, no. 2 (2020): 94–106. http://dx.doi.org/10.34306/itsdi.v2i2.387.

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Planning a tourism trip is an important part for tourists so that their tour is satisfying. Service bureaus that have a function to help provide information and prepare tourist travel plans for tourists often provide random destination choices because they do not know the pattern of selecting tourist destinations. This will be detrimental to tourists when service bureaus make wrong tourism travel plans. Tourists also often find it difficult to determine which tourist destination to go to because they do not know the environmental conditions in tourist destinations. To overcome this problem, in

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KRIBII, Rajae, and Youssef FAKIR. "Mining Frequent Sequential Patterns." Journal of Big Data Research 1, no. 2 (2021): 20–37. http://dx.doi.org/10.14302/issn.2768-0207.jbr-21-3455.

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In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequentia

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Fageeri, Sallam Osman, Mohammad Abu Kausar, and Arockiasamy Soosaimanickam. "MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 15–21. http://dx.doi.org/10.17762/ijritcc.v11i5s.6591.

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This Market Basket Analysis (MBA) is a data mining technique that uses frequent pattern mining algorithms to discover patterns of co-occurrence among items that are frequently purchased together. It is commonly used in retail and e-commerce businesses to generate association rules that describe the relationships between different items, and to make recommendations to customers based on their previous purchases. MBA is a powerful tool for identifying patterns of co-occurrence and generating insights that can improve sales and marketing strategies. Although a numerous works has been carried out

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Laberiano, Andrade-Arenas, and Yactayo-Arias Cesar. "Seismic trend analysis: a data mining approach for pattern prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2623–34. https://doi.org/10.11591/ijai.v13.i3.pp2623-2634.

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In the global context, seismic movements represent a constant for the population due to geophysical variability and other factors that make them possible, carrying with them the risk of losing innocent lives. The main purpose of our research is to apply data mining techniques to prevent seismic events of any magnitude to anticipate and mitigate future events. In the development of the research, we applied knowledge discovery database methodology. The clustering analysis results revealed the following: cluster 0 encompassed 45 items, with average magnitude of 0.230, representing 15.5% of the to

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Maryana, Sufiatul, and Lita Karlitasari. "Search Of Favorite Books As A Visitor Recommendation of The Fmipa Library Using CT-Pro Algorithm." Journal of Science Innovare 1, no. 01 (2018): 09–13. http://dx.doi.org/10.33751/jsi.v1i01.677.

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The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data

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Adda, Mehdi. "A Pattern Language for Knowledge Discovery in a Semantic Web context." International Journal of Information Technology and Web Engineering 5, no. 2 (2010): 16–31. http://dx.doi.org/10.4018/jitwe.2010040102.

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Ontologies are used to represent data and share knowledge of a specific domain, and in recent years they tend to be used in many applications such as database integration, peer-to-peer systems, e-commerce, semantic web services, bioinformatics, or social networks. Feeding ontological domain knowledge into those applications has proven to increase flexibility and inter-operability and interpretability of data and knowledge. As more data is gathered/generated by those applications, it becomes important to analyze and transform it to meaningful information. One possibility is to use data mining t

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Khin, Sein Hlaing, and Myo Kay Khine Thaw Yin. "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1604–6. https://doi.org/10.5281/zenodo.3591147.

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Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Data

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Gautam, Kudale, and Singh Rajpoot Sandeep. "CLUSTERING IN DATA MINING: TECHNIQUES, ADVANTAGES, APPLICATIONS, AND CHALLENGES." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 62–70. https://doi.org/10.5281/zenodo.10434263.

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<em>Clustering is a technique that groups similar data points together for analysis and pattern discovery across various fields like machine learning, data mining, and image analysis. Its main purpose is to group similar objects together based on a defined distance measure. Essentially, clustering involves partitioning a data set into subsets, with each subset containing data points that are similar to each other. This research paper aims to provide a comprehensive understanding of clustering in data mining. It discusses the concept of clustering, its advantages and disadvantages, applications

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Meddah, Ishak, and Belkadi Khaled. "Discovering Patterns using Process Mining." International Journal of Rough Sets and Data Analysis 3, no. 4 (2016): 21–31. http://dx.doi.org/10.4018/ijrsda.2016100102.

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Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or

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Andrade Arenas, Laberiano, and Cesar Yactayo-Arias. "Seismic trend analysis: a data mining approach for pattern prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2623. http://dx.doi.org/10.11591/ijai.v13.i3.pp2623-2634.

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<span lang="EN-US">In the global context, seismic movements represent a constant for the population due to geophysical variability and other factors that make them possible, carrying with them the risk of losing innocent lives. The main purpose of our research is to apply data mining techniques to prevent seismic events of any magnitude to anticipate and mitigate future events. In the development of the research, we applied knowledge discovery database methodology. The clustering analysis results revealed the following: cluster 0 encompassed 45 items, with average magnitude of 0.230, rep

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Hunyadi, Ioan Daniel, Nicolae Constantinescu, and Oana-Adriana Țicleanu. "Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques." Applied Sciences 15, no. 10 (2025): 5498. https://doi.org/10.3390/app15105498.

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Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish

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COBLE, JEFFREY, DIANE J. COOK, and LAWRENCE B. HOLDER. "STRUCTURE DISCOVERY IN SEQUENTIALLY-CONNECTED DATA STREAMS." International Journal on Artificial Intelligence Tools 15, no. 06 (2006): 917–44. http://dx.doi.org/10.1142/s0218213006003041.

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Historically, data mining research has been focused on discovering sets of attributes that discriminate data entities into classes or association rules between attributes. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event driven, such as counter-terrorism intelligence analysis. In this paper we describe an algorithm designed to operate over relational data received from a continuous stream. Our approach includes a mechanism for sum

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Singh, Kuldeep, and Bhaskar Biswas. "Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints." International Journal of Data Warehousing and Mining 15, no. 3 (2019): 1–27. http://dx.doi.org/10.4018/ijdwm.2019070101.

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High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of

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Selvam, S. "A New Algorithm for Pattern Based Using Mining Association Rules." Asian Journal of Computer Science and Technology 9, no. 2 (2020): 24–27. http://dx.doi.org/10.51983/ajcst-2020.9.2.2171.

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It is indeed an art to match maximum number of preferences by utilizing limited number of resources. During the current academic year 75% of the admissions to Engineering Colleges have gone down, as only 30% to 40% of intake has been filled. Without reaching the breakeven point, the management of the institution becomes a complicated issue. The main aim of this paper is to discover a pattern to identify the choice of preferences of the candidates to seek admissions in any academic institutions. For the purpose of matching optimum number of candidates to suit our existing system, we have design

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Krishna, Tiruveedula Gopi, Dr Mohamed Abdeldaiem Abdelhadi, and M. Madhusudhana Subramanian. "New Patterns And Techniques In Knowledge Discovery Through Data Mining." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 7, no. 1 (2013): 947–54. http://dx.doi.org/10.24297/ijmit.v7i1.714.

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The main focus of this paper discussion was on mining and its set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets. We discussed how a decision support process can be used to search for patterns of information in data. And also discussed different techniques for finding and describing structural patterns in data as well. Knowledge Discovery is a concept that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. We discussed all

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Djenouri, Youcef, Jerry Chun-Wei Lin, Kjetil Nørvåg, Heri Ramampiaro, and Philip S. Yu. "Exploring Decomposition for Solving Pattern Mining Problems." ACM Transactions on Management Information Systems 12, no. 2 (2021): 1–36. http://dx.doi.org/10.1145/3439771.

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This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strat

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