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The rapidly improving technologies like data mining and mobile technology need careful investigation in order to emerge these technologies. In this paper we identified the challenges confront by mobile data mining, visualization challenges, and mobile device limitations. The paper introduced a comprehensive framework that explores the idea, how we...
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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]. ...
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.
... Thus, it is not feasible to bring data mining tasks to mobile devices having limited resources. A study comprehensively discussed possible scenarios, where we can incorporate data mining techniques in mobile devices and the best architecture in the environment with current resources [13]. The motivation is to provide beneficial data mining results (required extracted information) in such a way that they are more useful to the end-user and according to their cognitive capabilities. ...
... We introduce a layered framework, namely Visual Mobile Data Mining (VMDM), as shown in Fig. 1. The enhanced framework combines both the information extraction model and the information visualization model with a brief description [13] [8]. The VMDM presents the main components and their interconnection involved in the mobile data mining environment scenarios, functions performed by different components and its sequence. ...
... There are some important features of mobile devices that help to find the required information easily and quickly. The intended objectives of this study are to achieve the features (discussed subsequently) by developing a functional prototype to validate, evaluate, and analyze the user's experience, which facilitates mobile device users with interactive, useful and informative information [13]. The study focuses on the following features; ...
The emergence of mobile and telecommunication technologies and the demand of data analytics lead the importance of mobile data mining in the last decade while the limitations of mobile devices great challenge in the reciprocated integration of these disciplines. Mobile devices are improving from the digital calendar to high capabilities computing devices that encourage the researchers to work in the mobile data mining environment continuously. In this paper, we discussed a Visual Mobile Data Mining (VMDM) framework, which incorporates both these emerging technologies to facilitate nomadic user for information. The study defined ten features that ensure information visualization features, i.e., functionality, effectiveness, efficiency, usability, and usefulness for interactive visualization techniques on mobile devices. The results are analyzed for the defined features and proposed interactive techniques used for descriptive data mining
data, and compared which showed promising results for both smartphone and tablet, three of the features are weak for the smartphone as compared to the tablet. The VMDM
framework is potentially incorporate different dimension in interdisciplinary research work especially, to other visualization techniques and web services to facilitate a wide range of mobile device users.
... The selection of evaluation technique depends on the nature of the research study. A number of techniques are discussed in [11] and categorized in various types based on their utilization in different domain [10]. The study evaluates the effectiveness of interactive technique in information visualization by considering visualization features, as briefly described in Table 1. ...
... In the study the data mining techniques are applied to the Amazon Books dataset published in 2008 [11]. The dataset has 7 years of data from year 2000 to year 2006, size of the dataset is 8.3 GB, contains user's feedback or reviews, helpful feedback, book ratings, user's ranking etc. ...
... Evaluation techniques can be categorized into several types based on their utilization in different domains [10]. In [11] contain comprehensive detail about evaluation techniques. ...
... 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. ...
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.
The purpose of the study is to introduce interactive mechanisms for information visualization techniques, which will assist the nomadic users in exploring information on small touch screen mobile devices like smartphones and tablets, on the move. The study is carried out by choosing the Posttest-Only Randomized Experimental research design and evaluated by using a questionnaire-based control experiment. The participants were asked to execute several tasks based on experiments on a functional prototype and fill the feature-based questionnaire. The study introduced six interactive visualization techniques to visualize information in a way that conveys the insight of the data effectively. The results indicate that Drill down approach in column chart (DDA+CC) and Legend navigation approach in column chart (LNA+CC) shows the reliable results and facilitate information manipulation on small screens of mobile devices, Drill down approach in bar chart (DDA+BC) and Legend navigation approach in bar chart (LNA+BC) shows weak results for Smartphones, the interactive visualization techniques DDA+LNA+CC and DDA+LNA+BC are highly appreciated for mobile devices, that are for both Smartphones and Tablets.
