Association based classification for relational data and its use in web mining

Bartík, Vladimír

doi:10.1109/cidm.2009.4938657

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Penelitian terkait klasifikasi pernah dilakukan oleh Niu (2009) menggunakan compactness of rule, dengan hasil penelitian dengan metode yang diusulkan memiliki hasil klasifikasi terbaik dalam perbandingannya dengan teknik klasifikasi dan asosiasi berdasar pada rule mining. Bartik (2009) pada penelitiannya mengusulkan teknik klasifikasi assosiation rule mining untuk data relational dan web mining. Hao (2009) menggunakan association rule sebagai peningkatan metode klasifikasi pada klasifikasi data Bank Direct Marketing.…”

Section: Pendahuluanunclassified

Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing

Oktanisa

Supianto

2018

JTIIK

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Klasifikasi merupakan teknik dalam data mining untuk mengelompokkan data berdasarkan keterikatan data terhadap data sampel. Pada penelitian ini, kami melakukan perbandingan 9 teknik klasifikasi untuk mengklasifikasi respon pelanggan pada dataset Bank Direct Marketing. Perbandingan teknik klasifikasi ini dilakukan untuk mengetahui model dalam teknik klasfikasi yang paling efektif untuk mengklasifikasi target pada dataset Bank Direct Marketing. Teknik klasifikasi yang digunakan yaitu Support Vector Machine, AdaBoost, Naïve Bayes, Constant, KNN, Tree, Random Forest, Stochastic Gradient Descent, dan CN2 Rule. Proses klasifikasi diawali dengan preprocessing data untuk melakukan penghilangan missing value dan pemilihan fitur pada dataset. Pada tahap evaluasi digunakan teknik 10 fold cross validation. Setelah dilakukan pengujian, didapatkan bahwa hasil klasifikasi menunjukkan akurasi terbaik diperoleh oleh model Tree, Constant, Naive Bayes, dan Stochastic Gardient Descent. Kemudian diikuti oleh model Random Forest, K-Nearest Neighbor, CN-2 Rule, AdaBoost dan Support Vector Machine. Dari keempat model yang menunjukkan hasil akurasi terbaik, untuk kasus ini Stochastic Gradient Descent terpilih sebagai model yang memiliki akurasi terbaik dengan nilai akurasi sebesar 0,972 dan hasil visualisasi yang dihasilkan lebih jelas untuk mengklasifikasi target pada dataset Bank Direct Marketing. AbstractClassification is a technique in data mining to classify data based on the attachment of data to the sample data.. In this paper, we present the comparison of 9 classification techniques performed to classify customer response on the dataset of Bank Direct Marketing. The techniques performed to find out the effectiveness model in the classification technique used to classify targets on the dataset of Bank Direct Marketing. The techniques used are Support Vector Machine, AdaBoost, Naïve Bayes, Constant, KNN, Tree, Random Forest, Stochastic Gradient Descent, and CN2 Rule. The classification process begins with preprocessing data to perform missing value omissions and feature selection on the dataset. Cross validation technique, with k value is 10, used in the evaluation stage. After testing, it was found that the classification results showed the best accuracy obtained when using the Tree model, Constant, Naive Bayes and Stochastic Gradient Descent. Afterwards the Random Forest model, K-Nearest Neighbor, CN-2 Rule, AdaBoost, and Support Vector Machine are followed. Of the four models with the high accuracy results, in this case Stochastic Gradient Descent was selected as the best accuracy model with an accuracy value of 0.972 and resulting visualization more clearly to classify targets on the dataset of Bank Direct Marketing.

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Section: Pendahuluanunclassified

Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing

Oktanisa

Supianto

2018

JTIIK

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“…The experimental results indicate that the CISRW algorithm based on related rule ordering techniques do well by means of accuracy of classification. Bartik et al, [3] proposed association rules in web data mining. The simulation results indicate that the proposed algorithm is a technique with better accuracy and human understandable classification scheme.…”

Section: Introductionmentioning

confidence: 99%

Association rule with frequent pattern growth algorithm for frequent item sets mining

Wisaeng¹

2014

ams

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Frequent item sets mining from the transaction dataset is one of the most challenging problems in data mining approaches. In many real world scenarios, the information is not extracted from a single data source, but from distributed and heterogeneous ones. Therefore, the discovered knowledge in this paper is generating association rules using frequent pattern growth algorithms for transactional market basket analysis dataset is presented. The process of rule discovery is illustrated on a dataset containing transactions of customers of the supermarket. The experimental results show that provides excellent market basket analysis performance even on a big data sets.

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Use of Frequent Itemset Mining Techniques to Analyze Business Processes

Bartík

Pospı́šil

2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Association based classification for relational data and its use in web mining

Cited by 3 publications

References 9 publications

Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing

Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing

Association rule with frequent pattern growth algorithm for frequent item sets mining

Use of Frequent Itemset Mining Techniques to Analyze Business Processes

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