Data Mining preparation: Process, Techniques and Major Issues in Data Analysis

Jassim, Mustafa Abdalrassual; Abdulwahid, Sarah Najm

doi:10.1088/1757-899x/1090/1/012053

Cited by 22 publications

(17 citation statements)

References 8 publications

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“…These techniques, still widely used today are often referred to as the traditional approaches to data integration and serve as core components of important data management projects such as building data warehouses and synchronizing data between applications [40]. The various scenarios of data integration across these numerous projects enable them to be mapped into three fundamental approaches namely consolidation, propagation and federation [41]. Data consolidation involves a wholesale transfer of data from one or more systems to another and is usually applied in business intelligence for centralizing data from multiple systems into a single data warehouse.…”

Section: Multidata Integration Techniquesmentioning

confidence: 99%

Multimedia Data Mining and Processing for News Source Attribution

Abimbola Owonipa,

Oladele Aro,

Adunni Olukiran

2024

Int J Res Rev

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The desire for unbiased journalism that effectively counters disinformation is widely recognised. News consumers are not only interested in news, but they also want unbiased journalism that cuts through disinformation, and they want it from trusted news sources. Consequently, media researchers need to explore ways to facilitate news-source identification, irrespective of the platform used. However, the availability of multimedia data sources has seen a remarkable surge in recent years, encompassing demographic data, social media data, geodata, and pervasive digital trace data. Multimedia data mining is a procedure of discovering stimulating trends via media data using video, text, and audio that are not generally available by simple enquiries and related outputs. Researchers face the challenge of integrating these diverse sources to enhance news source attribution in multimedia data including platforms like Facebook, WhatsApp and Instagram. The paper presents a review of multimedia data approaches and their application to news source attribution research. Also, the examination of the benefits and limitations of these techniques and discussion on future directions were mentioned. Consideration was on machine learning and statistical approaches to multimedia data, which include deep learning, and probabilistic modelling. Similarly, a discussion on the importance of data privacy and ethics in news source attribution research was stated. The contribution of this study is highly relevant for news media research groups striving to improve their capability to attribute sources in multimedia data, thereby combatting disinformation and amplifying trusted media brands. Keywords: Data mining, Multimedia, Data process, News source attribution, Unbiased journalism

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Section: Multidata Integration Techniquesmentioning

confidence: 99%

Multimedia Data Mining and Processing for News Source Attribution

Abimbola Owonipa,

Oladele Aro,

Adunni Olukiran

2024

Int J Res Rev

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“…Sebagai bentuk teknik ekstraksi data, data mining memiliki kemampuan untuk menemukan pola (pattern) yang tersembunyi guna menghasilkan pengetahuan baru dalam suatu kumpulan data yang bahkan abstrak sebelumnya [16], [17]. Secara khusus pendekatan data mining memiliki teknik-teknik tertentu berdasarkan tujuan pemanfaatan ekstraksi data, baik untuk kebutuhan estimasi, prediksi, klasifikasi, pengelompokan, maupun asosiasi [18]. Teknik data mining tidak Langkah pertama yang dilakukan adalah menentukan berapa banyak cluster yang akan dibuat, selanjutnya disebut sebagai nilai k. Tahap selanjutnya adalah menentukan nilai centroid (pusat cluster awal) yang diperoleh dari data set secara acak sejumlah nilai k. Selanjutnya menghitung jarak setiap dataset dengan masing-masing centroid.…”

Section: Data Miningunclassified

Pemetaan Kondisi Lingkungan Tanam menggunakan K-Means Clustering

Kholila¹,

Mujiono²,

Wahyudi³

2023

jsitik

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Melalui pendekatan clustering, data rekam yang terdiri dari waktu tanam, ph, suhu air, suhu udara, dan nilai TDS dapat dimanfaatkan guna memetakan kondisi lingkungan tanam. Melalui pemetaan kondisi lingkungan tanam, petani mendapatkan informasi tambahan terkait kondisi lingkungan tanam milik petani. Melalui informasi tersebut, diharapkan petani dapat memberikan tindakan pertanian yang sesuai dengan kondisi lingkungan tanam, lebih efektif dan efisien. Pemetaan kondisi lingkungan tanam menggunakan k-means clustering algorithm dalam 3 (tiga) cluster. Masing-masing merupakan cluster kondisi lingkungan tanam yang kurang nutrisi dan kurang air, cukup nutrisi tetapi kurang air, serta cukup nutrisi dan cukup air. Cluster testing menggunakan elbow method menunjukkan bahwa jumlah cluster optimal dalam pengelompokan K-Means adalah 3 cluster dengan nilai inersia 199.065.

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“…• Data Mining: To extract useful patterns, techniques and tools must be applied at this point in the process. Classification, clustering, regression, and other techniques are all part of data mining algorithms [47].…”

Section: Figurementioning

confidence: 99%

Handling Big Data in Education: A Review of Educational Data Mining Techniques for Specific Educational Problems

Ampadu

2023

ACRT

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In the era of big data, where the amount of information is growing exponentially, the importance of data mining has never been greater. Educational institutions today collect and store vast amounts of data, such as student enrollment and attendance records, and their exam results. With the need to sift through enormous amounts of data and present it in a way that anyone can understand, educational institutions are at the forefront of this trend, and this calls for a more sophisticated set of algorithms. Data mining in education was born as a response to this problem. Traditional data mining methods cannot be directly applied to educational problems because of the special purpose and function they serve. Defining at-risk students, identifying priority learning requirements for varied groups of students, increasing graduation rates, monitoring institutional performance efficiently, managing campus resources, and optimizing curriculum renewal are just a few of the applications of educational data mining. This paper reviews methodologies used as knowledge extractors to tackle specific education challenges from large data sets of higher education institutions to the benefit of all educational stakeholders.

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Data Mining preparation: Process, Techniques and Major Issues in Data Analysis

Cited by 22 publications

References 8 publications

Multimedia Data Mining and Processing for News Source Attribution

Multimedia Data Mining and Processing for News Source Attribution

Pemetaan Kondisi Lingkungan Tanam menggunakan K-Means Clustering

Handling Big Data in Education: A Review of Educational Data Mining Techniques for Specific Educational Problems

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