Data Mining: Data Mining Concepts and Techniques

Agarwal, Shivam

doi:10.1109/icmira.2013.45

Cited by 160 publications

(75 citation statements)

References 0 publications

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“…Šio metodo pagrindinis uždavinys yra pagal pateiktus objekto duomenis sukurti duomenų žymėjimo algoritmą, kuriuo remiantis duomenys bus skirstomi į klases. Duomenų klasifikavimas yra dviejų žingsnių metodas (Agarwal, 2013 Dar vienas mašininio mokymosi metodas, taikomas duomenų tyryboje, yra klasterizacija. Taikant šį metodą tarp objektų yra ieškoma panašumų, o juos radus -panašūs ir skirtingi objektai skirstomi į atskiras grupes (Liu, Li, Xiong, Gao ir Wu, 2010).…”

Section: Didžiųjų Duomenų Analizės Metodaiunclassified

Didžiųjų Duomenų Panaudojimas Socialinėje Medijoje / the Big Data Use in Social Media

Karpovičiūtė

Sabaitytė

2019

Science - Future of Lithuania

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The digital revolution and the communication platforms provided by the web 2.0 virtual space era, such as social media, social networks, other tools and channels, create new opportunities for better marketing decisions based on user-generated data analysis. Every day customers of social media and other virtual tools are creating huge amounts of their actions caused data, and business lack management tools for the support of this process, which could create knowledge in the area of customer profiles and preferences deeper cognition. Growing numbers of social media users indicate the popularity of these communication tools among the information society, but science today lacks a deeper knowledge of social media generated data and other algorithms for this data usage. Therefore, the purpose of the article is defined as the development of the conceptual model of big data generated by social media usage in business. The formation of the conceptual model is based on the analysis of big data assumptions and application possibilities, social media classification peculiarities and different channel specifics, identification of big data analysis methods and analysis of large data applications generated by social media. The conceptual model creates preconditions for deeper knowledge of user-generated big data in nowadays widely used communication platforms, as well as creation of the decision support tool for marketing specialists in order to use big data from social media in deeper customer profile and preferences cognition. Methods employed in this research are: literature and other references analysis, synthesis and logical analysis of information, comparison of information, systemization and visualization.

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Section: Didžiųjų Duomenų Analizės Metodaiunclassified

Didžiųjų Duomenų Panaudojimas Socialinėje Medijoje / the Big Data Use in Social Media

Karpovičiūtė

Sabaitytė

2019

Science - Future of Lithuania

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“…The tree always starts with the single node containing training datasets [16]. If the tuples in a dataset belongs to the same class then the node turns into a leaf, labeled with that corresponding class [16]. Otherwise, an attribute selection method is used to determine the splitting criterion.…”

Section: A Decision Treesmentioning

confidence: 99%

“…The node at the topmost of the tree called the root node which represents the entire datasets [2], [4]. The tree always starts with the single node containing training datasets [16]. If the tuples in a dataset belongs to the same class then the node turns into a leaf, labeled with that corresponding class [16].…”

Section: A Decision Treesmentioning

confidence: 99%

“…Classification is specifically used for predicting the unknown class label of data objects [16]. This is considered as the most commonly applied technique [6].…”

Section: Data Mining Approaches and Techniquesmentioning

confidence: 99%

“…Otherwise, an attribute selection method is used to determine the splitting criterion. Such a method may use a heuristic or statistical measure (e.g., information gain or gini index) to select the best way to separate the tuples into individual classes [16].…”

Section: A Decision Treesmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Engineering Students Pedagogical Progress

Mahboob¹,

Syed²,

Ur³

et al. 2018

ijacsa

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Abstract-Students' pedagogical progress plays a pivotal role in any educational institute in order to pursue imperative education. Educational institutes, Universities, Colleges implement various performance measures in order to keep analyzing and tracking progress of students to cultivate benefits of education in a better way. There are several data mining techniques to apply on education in order to build constructive educational strategies and solutions. This study aims to analyze and track engineering under graduate student's records to judge quality education, student motivation towards learning, and student pedagogical progress to maintain education at high quality level and predicting engineering student's forthcoming progress. Different engineering discipline students' (of three different cohorts) data have been analyzed for tracing current as well as future pedagogical progress based on their sessional (preexamination) marks. In this research, the classification techniques by k-nearest neighbor, Naïve Bayes and decision trees are applied to evaluate different engineering technologies student's performance and also there are different methodologies that can be used for data classification.

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