Comparisons Of Data Mining Classification Algorithms For Customers' Shopping Intention In E-Commerce

2021 ◽  
Author(s):  
Kek Zhi Xuan ◽  
Shuhaida Ismail ◽  
Intan Syazwani Noorain ◽  
Nur Aliaa Dalila A. Muhaime
Keyword(s):  
Data Mining ◽  
Classification Algorithms

scholarly journals Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 95
Author(s):  
Heba Kurdi ◽  
Amal Al-Aldawsari ◽  
Isra Al-Turaiki ◽  
Abdulrahman S. Aldawood
Keyword(s):  
Data Mining ◽  
Plant Size ◽  
Support Vector ◽  
Classification Algorithms ◽  
Palm Tree ◽  
Rhynchophorus Ferrugineus ◽  
Red Palm Weevil ◽  
Palm Weevil ◽  
Using Data ◽  
F Measure

In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.

A method for improving the accuracy of data mining classification algorithms

2009 ◽  
Vol 36 (10) ◽  
pp. 2829-2839 ◽  
Author(s):  
Nikolaos Mastrogiannis ◽  
Basilis Boutsinas ◽  
Ioannis Giannikos
Keyword(s):  
Data Mining ◽  
Classification Algorithms

scholarly journals Comparing sets of patterns with the Jaccard index

2018 ◽  
Vol 22 ◽  
Author(s):  
Sam Fletcher ◽  
Md Zahidul Islam
Keyword(s):  
Data Mining ◽  
Driving Force ◽  
Prediction Accuracy ◽  
Jaccard Index ◽  
Classification Algorithms ◽  
Single Element ◽  
Temporal Data ◽  
Real World Data ◽  
Actionable Knowledge ◽  
Computational Simplicity

The ability to extract knowledge from data has been the driving force of Data Mining since its inception, and of statistical modeling long before even that. Actionable knowledge often takes the form of patterns, where a set of antecedents can be used to infer a consequent. In this paper we offer a solution to the problem of comparing different sets of patterns. Our solution allows comparisons between sets of patterns that were derived from different techniques (such as different classification algorithms), or made from different samples of data (such as temporal data or data perturbed for privacy reasons). We propose using the Jaccard index to measure the similarity between sets of patterns by converting each pattern into a single element within the set. Our measure focuses on providing conceptual simplicity, computational simplicity, interpretability, and wide applicability. The results of this measure are compared to prediction accuracy in the context of a real-world data mining scenario.

scholarly journals A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

2020 ◽  
pp. 11-18
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Support Vector ◽  
Classification Algorithms ◽  
End User ◽  
Preparation Methods ◽  
Survey Paper ◽  
Vector Machines ◽  
Feature Selection Techniques ◽  
Phishing Detection

: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.

Handling Fuzzy Similarity for Data Classification

2011 ◽  
pp. 796-802 ◽  
Author(s):  
Roy Gelbard ◽  
Avichai Meged
Keyword(s):  
Data Mining ◽  
Similarity Measure ◽  
Data Representation ◽  
Classification Algorithms ◽  
Binary Representation ◽  
Fuzzy Data ◽  
Problem Domain ◽  
Data Types ◽  
Fuzzy Similarity ◽  
Set Up

Representing and consequently processing fuzzy data in standard and binary databases is problematic. The problem is further amplified in binary databases where continuous data is represented by means of discrete ‘1’ and ‘0’ bits. As regards classification, the problem becomes even more acute. In these cases, we may want to group objects based on some fuzzy attributes, but unfortunately, an appropriate fuzzy similarity measure is not always easy to find. The current paper proposes a novel model and measure for representing fuzzy data, which lends itself to both classification and data mining. Classification algorithms and data mining attempt to set up hypotheses regarding the assigning of different objects to groups and classes on the basis of the similarity/distance between them (Estivill-Castro & Yang, 2004) (Lim, Loh & Shih, 2000) (Zhang & Srihari, 2004). Classification algorithms and data mining are widely used in numerous fields including: social sciences, where observations and questionnaires are used in learning mechanisms of social behavior; marketing, for segmentation and customer profiling; finance, for fraud detection; computer science, for image processing and expert systems applications; medicine, for diagnostics; and many other fields. Classification algorithms and data mining methodologies are based on a procedure that calculates a similarity matrix based on similarity index between objects and on a grouping technique. Researches proved that a similarity measure based upon binary data representation yields better results than regular similarity indexes (Erlich, Gelbard & Spiegler, 2002) (Gelbard, Goldman & Spiegler, 2007). However, binary representation is currently limited to nominal discrete attributes suitable for attributes such as: gender, marital status, etc., (Zhang & Srihari, 2003). This makes the binary approach for data representation unattractive for widespread data types. The current research describes a novel approach to binary representation, referred to as Fuzzy Binary Representation. This new approach is suitable for all data types - nominal, ordinal and as continuous. We propose that there is meaning not only to the actual explicit attribute value, but also to its implicit similarity to other possible attribute values. These similarities can either be determined by a problem domain expert or automatically by analyzing fuzzy functions that represent the problem domain. The added new fuzzy similarity yields improved classification and data mining results. More generally, Fuzzy Binary Representation and related similarity measures exemplify that a refined and carefully designed handling of data, including eliciting of domain expertise regarding similarity, may add both value and knowledge to existing databases.

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