Comparison of association rule mining with pruning and adaptive technique for classification of phishing dataset

2013 ◽  
Author(s):  
A.S. Kadam ◽  
S.S. Pawar
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Adaptive Technique

scholarly journals A Novel Framework to Use Association Rule Mining for classification of traffic accident severity

2017 ◽  
Vol 13 (21) ◽  
pp. 37-44 ◽  
Author(s):  
Meenu Gupta ◽  
Vijender Kumar Solanki ◽  
Vijay Kumar Singh
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prevention Measures ◽  
Rule Mining ◽  
Data Set ◽  
Accident Severity ◽  
Institute Of Technology

Introduction: Traffic accidents are an undesirable burden on society. Every year around one million deaths and more than ten million injuries are reported due to traffic accidents. Hence, traffic accidents prevention measures must be taken to overcome the accident rate. Different countries have different geographical and environmental conditions and hence the accident factors diverge in each country. Traffic accident data analysis is very useful in revealing the factors that affect the accidents in different countries. This article was written in the year 2016 in the Institute of Technology & Science, Mohan Nagar, Ghaziabad, up, India. Methology: We propose a framework to utilize association rule mining (arm) for the severity classification of traffic accidents data obtained from police records in Mujjafarnagar district, Uttarpradesh, India. Results: The results certainly reveal some hidden factors which can be applied to understand the factors behind road accidentality in this region. Conclusions: The framework enables us to find three clusters from the data set. Each cluster represents a type of accident severity, i.e. fatal, major injury and minor/no injury. The association rules exposed different factors that are associated with road accidents in each category. The information extracted provides important information which can be employed to adapt preventive measures to overcome the accident severity in Muzzafarnagar district.

scholarly journals Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

2017 ◽  
Vol 6 (3) ◽  
pp. 250-255
Author(s):  
Razia Sulthana A ◽  
Subburaj Ramasamy
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
The Internet ◽  
Fuzzy Logics ◽  
Rule Mining ◽  
Customer Reviews ◽  
The Relationship ◽  
New Framework

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.

Interestingness Measures for Association Rules

2008 ◽  
pp. 36-58
Author(s):  
Yun Sing Koh ◽  
Richard O’Keefe ◽  
Nathan Rountree
Keyword(s):  
Experimental Study ◽  
Selection Bias ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Information Gain ◽  
Experimental Results ◽  
Rule Mining ◽  
Interestingness Measures

Association rules are patterns that offer useful information on dependencies that exist between the sets of items. Current association rule mining techniques such as apriori often extract a very large number of rules. To make sense of these rules we need to order or group the rules in some fashion such that the useful patterns are highlighted. The study of this process involves the investigation of an “interestingness” in the rules. To date, various measures have been proposed but unfortunately, these measures present inconsistent information about the interestingness of a rule. In this chapter, we show that different metrics try to capture different dependencies among variables. Each measure has its own selection bias that justifies the rationale for preferring it compared to other measures. We present an experimental study of the behaviour of the interestingness measures such as lift, rule interest, Laplace, and information gain. Our experimental results verify that many of these measures are very similar in nature. From the findings, we introduce a classification of the current interestingness measures.

scholarly journals Why do people call crisis helplines? Identifying taxonomies of presenting reasons and discovering associations between these reasons

2020 ◽  
Vol 26 (4) ◽  
pp. 2597-2613 ◽  
Author(s):  
Robin Turkington ◽  
Maurice D Mulvenna ◽  
Raymond R Bond ◽  
Siobhan O’Neill ◽  
Courtney Potts ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
International Classification Of Diseases ◽  
Rule Mining ◽  
Data Set ◽  
Log Data ◽  
Classification Of Diseases ◽  
Exploratory Data ◽  
Telephone Crisis

The objective of this study is to identify the most common reasons for contacting a crisis helpline through analysing a large call log data set. Two taxonomies were identified within the call log data from a Northern Ireland telephone crisis helpline (Lifeline), categorising the cited reason for each call. One taxonomy categorised the reasons at a fine granular level; the other taxonomy used the relatively coarser International Classification of Diseases–10. Exploratory data analytic techniques were applied to discover insights into why individuals contact crisis helplines. Risk ratings of calls were also compared to assess the associations between presenting issue and of risk of suicide as assessed. Reasons for contacting the service were assessed across geolocations. Association rule mining was used to identify associations between the presenting reasons for client’s calls. Results demonstrate that both taxonomies show that calls with reasons relating to suicide are the most common reasons for contacting Lifeline and were a prominent feature of the discovered association rules. There were significant differences between reasons in both taxonomies concerning risk ratings. Reasons for calling helplines that are associated with higher risk ratings include those calling with a personality disorder, mental disorders, delusional disorders and drugs (legal). In conclusion, employing two differing taxonomy approaches to analyse call log data reveals the prevalence of main presenting reasons for contacting a crisis helpline. The association rule mining using each taxonomy provided insights into the associations between presenting reasons. Practical and research applications are discussed.

Loss Profit Estimation Using Temporal Association Rule Mining

2016 ◽  
Vol 3 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Timing Constraints ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Temporal Association Rule ◽  
Frequent Items

Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable items with the help of ranking found in the paper. An example is illustrated to validate the results.

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