Discovering “interesting” keyword patterns in Hadith chapter documents

Zainol, Zuraini; Nohuddin, Puteri N. E.; Jaymes, Mohd T. Hamid; Marzukhi, Syahaneim

doi:10.1109/icictm.2016.7890785

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…According to [27], Text Mining (TM) can be defined as an analytic process that is designed to explore the unstructured text documents in search of useful information and knowledge hidden from a large amount of text resources. TM has been widely applied in many application domains such as military knowledge [28][29][30][31], business analytics [32][33][34], documents analysis [35][36][37], social media issues [38][39][40], etc.…”

Section: Background and Related Workmentioning

confidence: 99%

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

Zainol

Nohuddin²,

Rasid

et al. 2019

IJACSA

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The purpose of this study is to analyse the keyword relationships of paediatric cancer patient's experiences whilst being hospitalised during the treatment session. This study collects data through 40 days of observations on 21 paediatric cancer patients. A combination of text analytical visualizations such as network analysis map and bubble graph to analyse the data is applied in this study. Through the analysis, keywords such as "cri" (crying), "lay" (laying), "sleep" (sleeping) and "watch" (watching) are found the most common activities that have been experienced by paediatric cancer patients when they were hospitalised. Based on this observation, it can be argued that these activities can be represented as the experience that they have whilst being in the hospital. Based on the findings, hospitalised paediatric cancer patient's experience is limited due to the treatment protocol that requires them to be attached to intravenous line. Therefore, most of their activities are focused in bed such as sleeping, playing with their mobile, watching video and so on in bed. This study also offers a novel approach of transforming cancer patient data into useful knowledge about keyword relationship in paediatric cancer patient's experience during their stay in the hospital. The incorporation of these two text analytics offers insights for researchers to understand the interesting hidden knowledge in the collection of unstructured data, and this information can be used by medical providers, psychologists, games designers and others to develop any applications that can assist their difficulties and ease their pain while warded in the hospital.

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Section: Background and Related Workmentioning

confidence: 99%

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

Zainol

Nohuddin²,

Rasid

et al. 2019

IJACSA

Self Cite

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“…Such valuable information and patterns may assist the top level managers in decision making. DM has been applied in various application areas such as market based analysis, healthcare [5], smart homes [6], business, text documents [7][8][9][10], environmental studies [11,12], flood detection [13], crime investigation, fraud detection, geology, food microbiology, astronomy, etc. Researchers [14] summarized some common data mining tasks and techniques (see Table 1).…”

Section: Data Mining and Knowledge Discovery In Databasementioning

confidence: 99%

Case Study of UPNM Students Performance Classification Algorithms

B. Rahayu,

D. Kamarudin,

Zainol

2018

IJET

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Most students have a problem to keep track on their learning performance.Â Some lecturers with high teaching hours and burden of administration jobs may have difficulty to identify weak and low performance students. In this study, three classification techniques are applied on educational datasets to predict the studentsâ€™ performance based on coursework assessments. Thus, this prediction results may help lecturers and students to improve their teaching and learning process. The objective of study is to predict studentsâ€™ performance based on coursework assessments using classification algorithms. The selected classification algorithms applied in this study such as J48 Decision Tree, NaÃ¯ve Bayes and kNN. WEKA is used as an experimental tool. The selected algorithms are applied on a data of student database of Data Mining subject. Findings shows NaÃ¯ve Bayes outperforms other classification algorithms with above 80% prediction rate. Thus, the studentsâ€™ performance for Data Mining Subject is improved. As a conclusion, the classification algorithms can predict studentsâ€™ performance on a particular subject based on coursework assessments.Â Â

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“…It aims to extract the interesting relationship, frequent patterns and coloration existing in the set of items in the data repository [8]. ARM has been applied in many real world problems such as finding patterns in documents [9][10], predicting floods [11], trend analysis of social networks [12], monitoring elderly people [13], etc. Various research has been done to see the suitability of data mining in predicting the climate.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Climate Variability in Malaysia Using Association Rule Mining

Rashid¹,

Nohuddin²,

Zainol³

2018

IJET

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Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.

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Discovering “interesting” keyword patterns in Hadith chapter documents

Cited by 9 publications

References 4 publications

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

Case Study of UPNM Students Performance Classification Algorithms

Analyzing Climate Variability in Malaysia Using Association Rule Mining

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