Penerapan Metode Heuristic Semisupervised Fuzzy Co-Clustering Algorithm With Ruspini’s Condition (Ss-Hfcr) Untuk Pengelompokan Dokumen Teks

Munif, Syahrul (2014) Penerapan Metode Heuristic Semisupervised Fuzzy Co-Clustering Algorithm With Ruspini’s Condition (Ss-Hfcr) Untuk Pengelompokan Dokumen Teks. Undergraduate thesis, Institut Teknologi Sepuluh Nopember Surabaya.

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Abstract

Dokumen merupakan sebuah tulisan yang memuat informasi. Banyaknya dokumen bisa menjadi suatu masalah tersendiri dalam mengelompokkannya. Pengelompokan dokumen merupakan bagian dari ilmu machine learning. Pengelompokkan bertujuan untuk mengatur dokumen supaya bisa terkelompok dalam bagianbagian kelompok / kategori. Heuristic Semi-supervised Fuzzy Coclustering Algorithm with Ruspini’s Condition (SS-HFCR) merupakan salah satu teknik baru dalam pengelompokan dokumen. Metode ini menggabungkan metode fuzzy clustering, co-clustering dan pengelompokan semi-supervised. Pada Tugas Akhir ini menggunakan metode SS-HFCR untuk mengelompokan data teks. Metode ini menghasilkan akurasi yang cukup baik untuk mengelompokan data WebKb dan Reuters-21578 R8 ========================================================================================== Recently, the document clustering is one of the important issues on data mining fields related with increasing the number of documents. Document clustering uses cluster analysis based on textual documents. Clustering methods is used to automatically cluster the retrieved documents according to its group. Heuristic Semi-supervised Algorithm Fuzzy Co-clustering with Ruspini's Condition (SS-HFCR) is one of the new techniques in document clustering. This method combines fuzzy clustering, co clustering and semi-supervised clustering. SS-HFCR uses the existing prior knowledge as rules in the form paired of documents. Each rule are set specifically whether documents have pair rule "must link" or "cannot link" in one group. The purpose of this algorithm is clustering of documents according to its group. The experimental results show that the SS-HFCR obtains good results for WebKb and Reuter-21578 R8 datasets

Item Type: Thesis (Undergraduate)
Additional Information: RSIf 005.740 68 Mun p
Uncontrolled Keywords: Clustering, Co-Culstering, Fuzzy, Machine learning, Semi-supervised
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.546 Computer algorithms
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions: Faculty of Information Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User: EKO BUDI RAHARJO
Date Deposited: 06 Oct 2020 04:03
Last Modified: 06 Oct 2020 04:03
URI: https://repository.its.ac.id/id/eprint/82079

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