Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Delima, Allemar Jhone P.

doi:10.18178/ijmlc.2019.9.4.837

Cited by 15 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to this study, data mining shows the relationships between data [3]. Data mining techniques are widely used in many areas for many purposes, such as predicting short-term adverse events in phototherapy treatments [4] or predicting index and non-index crimes in cities [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Patents in Cyber Security with Text Mining

Özata¹,

Demir²,

Doğan³

2021

IJCTE

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Patents in Cyber Security with Text Mining

Özata¹,

Demir²,

Doğan³

2021

IJCTE

View full text Add to dashboard Cite

“…In the era of data mining [6]- [8] and machine learning, data reduction procedures play vital importance [9]. It aims to obtain a fast, accurate, and adaptable model that quickly respond to incoming changes due to low computational complexity [3].…”

Section: Related Literaturementioning

confidence: 99%

Rank-Based Variable Minimization Using Clustering Algorithm

Delima¹,

Francisco²

2019

IJMLC

Self Cite

View full text Add to dashboard Cite

This paper introduces variable minimization with the advent of data mining techniques, particularly the cluster analysis using the K-means algorithm. The results of the simulation process validated the actual ranking of the variables in each category from the 407 records of student-respondents. A noticeable decrease of variables in determining the instructional performance of teachers in the Caraga Region, Philippines is evident in the simulation results. The variables that were clustered by the algorithm that conforms to have the lowest rank based on the actual survey was removed. Results showed that there were a total of fourteen (14) variables removed from the thirty-item survey questionnaire. In this case, it only proved that like any other data mining algorithms, the cluster analysis, particularly with the K-Means algorithm, is also effective in determining what variables in the data set to omit based on the groupings with the lowest rank it generated.

show abstract

“…Prediction in research serves as a powerful tool in the process of planning that can provide the researcher with a likelihood of future events. Technically, this data mining [1][2] approach is driven by using experiences and applying statistical, mathematical, or computational methods [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Delima¹

2020

IJACSA

Self Cite

View full text Add to dashboard Cite

The K-Nearest Neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employed variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The Genetic Algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem, which is its mating scheme bounded on its crossover operator. Thus, the use of the novel Inversed Bi-segmented Average Crossover (IBAX) was observed. In the present work, the crossover improved genetic algorithm (CIGAL) was instrumental in the enhancement of KNN's prediction accuracy. The use of the unmodified genetic algorithm had removed 13 variables; while the CIGAL then further removed 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improved the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN); the use of the lone KNN algorithm, the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique revealed a 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. The above results show that the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.

show abstract

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Cited by 15 publications

References 18 publications

Analysis of Patents in Cyber Security with Text Mining

Analysis of Patents in Cyber Security with Text Mining

Rank-Based Variable Minimization Using Clustering Algorithm

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Contact Info

Product

Resources

About