Framework for Interactive Data Mining Results Visualization on Mobile Devices

Khan, Muzammil; Shah, Ali Hassan; Ahmad, Israr

doi:10.14257/ijdta.2014.7.4.03

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…In surveys, questionnaire is used to collect the data using functional prototype over sample of real users selected from population and generalize the results to the whole population. Questionnaire used five-point Liker scale which is commonly used in questionnaires, and is the most widely used scale in survey research [5,4,8,9,10,11]. and Label readability and understand ability [12,13,14].…”

Section: Participantsmentioning

confidence: 99%

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Khan¹,

Khan²

2017

IJAEMS

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Section: Participantsmentioning

confidence: 99%

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Khan¹,

Khan²

2017

IJAEMS

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“…Evaluation techniques can be categorized into several types based on their utilization in different domains [10]. In [11] contain comprehensive detail about evaluation techniques.…”

Section: Visualization Evaluation Techniquementioning

confidence: 99%

“…In this study the data mining tasks are applied to the Amazon Books data set published in 2008 [11]. The data set have 7 years of data from year 2000 to year 2006, size of the data set is 8.3 GB, contains user's feedback or reviews, helpful feedback, book ratings, user's ranking etc.…”

Section: Data Source Domainmentioning

confidence: 99%

Single Level Drill Down Interactive Visualization Technique for Descriptive Data Mining Results

Khan

Hussain

Khan

2014

IJGDC

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Information technology plays vital role to enhance our knowledge and improve social life. Information presentation is as important as information itself, and interaction with these information enable one to understand these information quickly and easily. In this article, the information is explore up to one level granularity by introducing single level drill down interactive technique for descriptive data mining tasks results in order to convey inside of the data quickly, easily and effectively. The experimentation being done on Amazon dataset and two information visualization techniques i.e. column charts and bar charts. The interactive technique is comprehensively analyzing in both visualization techniques with respect to the visualization features. The drill down approach in column chart shows better performance comparatively.

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“…e main goal of data mining is to find out fascinating and concealed knowledge in the data and summarize it in a significant form [2][3][4]. Similarly, the results should be in the form that conveys the inside information effectively [5][6][7]. erefore, classification techniques are among the most important and commonly used techniques in data mining, and supervised class prediction techniques allow nominal class labels for predictions [8].…”

Section: Introductionmentioning

confidence: 99%

Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques

2022

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The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis (EDA) is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well.

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Framework for Interactive Data Mining Results Visualization on Mobile Devices

Cited by 4 publications

References 4 publications

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Single Level Drill Down Interactive Visualization Technique for Descriptive Data Mining Results

Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques

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