Informatics Publishing Limited

Navdeep Kaur ¹, Williamjeet Singh ¹

Affiliations
1 Punjabi University Patiala, Punjab, IN

Abstract

Objectives/Background: Addiction of alcohol is a complex disease which results from diversity of social, genetic and environmental influences. A report by World Health Organization, WHO (2014) estimates that most of the deaths are from alcohol related causes.The objective of this study is to analyze the alcoholic behavior of different age group people on the basis of risk factors. In this paper, we construct a comparative model of different classification techniques to analyze the best algorithm for predicting the alcoholic behavior of a person. Methods: Under this context, random tree and J48 that are decision tree algorithms have been exercised on the dataset of 600 people that is collected through a structured questionnaire by visiting de addicted centers, colleges, villages, government offices, old age homes of Patiala, Punjab. Findings: Results conclude that the random tree provides more precise results than J48 for all the age group people. Risk factors that come out to be most effective are impulsive nature, sensation seeking nature, financial loss, family conflict, depression, child abuse, alcoholic shop near home distance.The overall accuracy of random tree is 75.94% and for J48 is 71.26%. Applications/Improvement: There is a need to develop some intelligent tools in this area and the rules extracted from this analysis can be further used for designing the tool. More attributescan be incorporated to achieve the optimal results for predicting the behavior of an alcoholic person.

Keywords

Addiction, Classification, Data Mining, Prediction.

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Rekha ¹, Williamjeet Singh ²

Affiliations
1 Department of Computer Engineering, Punjabi University, Patiala, Punjab, IN
2 Department of Computer Engineering, Punjabi University, Patiala, Punjab, IN

Abstract

Opinion mining additionally known as sentiment analysis may be a method of finding users opinion regarding specific topic or a product or drawback. A subject may be a product, movie, news, event, location building etc. Opinion mining may be a field in data processing, natural language process (NLP), and net mining discipline. An outsized volume of data in on-line systems is hold on within the any kind format. This data takes a structured form that can be transmitted on the net, being the foremost common illustration kind and simple to understand by the individuals. In this paper, we have reviewed the mining process for getting customer's review regarding a particular mobile phone. Online Reviews from totally different sites that permit the net users to create their call concerning the merchandise they require buying can be collected from different selling sites and a comparison can be done in the marketing trends of a particular mobile. These reviews can be positive, negative and neutral. It's become quite tough to choose a particular phone as there are numerous available in the market since we have a tendency to unable to choose quickly. So from customer reviews we can compare them and can buy the best match from the information which can be provided by an algorithm on the collected data. Therefore it's obligatory to classify the reviews from structured information sets for analysis and opinion mining of any applications. In future work, we will propose an efficient algorithm which can easily provide the necessary information from collected data. A significant a part of our information-gathering is to search out what others suppose. With the growing availableness of user's reviews on totally different resources like on-line review sites and private blogs, new opportunities and demands seems as individuals currently will, and do, actively use data technologies to look out and perceive the opinions of others.

Keywords

Opinion Mining, Sentiment Analysis.

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Gurmeet Kaur ¹, Williamjeet Singh ¹

Affiliations
1 Department of CSE, Punjabi University, Patiala, IN

Abstract

Data mining is widely used in educational field to find the problems arise in this field is called Educational data mining (EDM). The higher institutes aimed to provide quality education to its student and to provide excellent graduates for the country growth. Best way to achieve this level quality education is to analyze the student academic performance. This analysis will help to identify students who are at risk and needs special attention. But student performance depends upon various factors. Number of studies has been carried out on these influencing factors. In this paper, we explore five categories of factors which positively or negatively affect the student performance. These categories are: Family information, Academic information, Personality, Management skill and Miscellaneous. This study will help the students to know their weak points which degrade their performance and also helpful for faculty and institutes management to counsel and motivate the students to improve their abilities who would be found at high risk of failure.

Keywords

Data Mining, Educational Data Mining, Academic Performance, Family Information, Academic Information, Personality, Management Skill.

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Sarpreet Kaur ¹, Sheena ¹, Williamjeet Singh ¹

Affiliations
1 Department of Computer Engineering, Punjabi University, Patiala, IN

Abstract

Big Data is hastily exploding in all domains, including physical and biological. Big Data Mining is the real world data mining. Traditional databases systems are not able to store and process big data. With increase in the size of data, amount of irrelevant data also increases and the process of mining becomes inaccurate. Big Data technologies are important in providing precise analysis, which lead to firm decision-making. Various Big Data technologies are available in market from different vendors including Amazon, IBM, Microsoft and Apache etc. The huge size of analytics requires large computation which can be done with the help of distributed processing HADOOP. This paper describes big data and processing of big data using RHADOOP.

Keywords

Hadoop, Hadoop Distributed File System (HDFS), HBASE, rhdfs, rmr, rhbase, RHadoop.

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