A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data

Tjhi, William-Chandra; Chen, Li Hui

doi:10.1016/j.fss.2007.10.003

Cited by 36 publications

(15 citation statements)

References 15 publications

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“…Algoritma Heuristic Fuzzy Co-clustering digunakan pada banyak data yang bi-partisi, misalnya data bi-partisi berdasarkan gambar spektral dan juga dokumen teks yaitu frekuensi kata yang terdapat dalam suatu dokumen (Yan, 2013). Disamping itu, Algoritma Heuristic Fuzzy Coclustering memiliki kelebihan untuk dapat menghasilkan group yang tidak bersifat hard cluster, yaitu anggota group yang satu dapat menjadi bagian dari group yang lain (Tjhi, 2008). Adapun tujuan dari penelitian ini adalah untuk melakukan perbandingan antara algoritma yang telah ada HFCR dengan modifikasi algoritma yang diajukan, HFCR dengan menerapkan metode semisupervised pada proses pengelompokan dokumen.…”

Section: Pendahuluanunclassified

“…Dalam penelitian yang telah dilakukan sebelumnya (Tjhi, 2008), penelitian dilakukan untuk dapat memberikan jalan keluar terhadap permasalahan yang timbul dalam penerapan Algoritma Fuzzy Clustering for Categorical Multivariate Data (FCCM) dan Fuzzy CoClustering of Document and Keyword (Fuzzy Codok), yang menerapkan pendekatan partitioning-ranking. Permasalahan pertama adalah akurasi dalam proses pengelompokan.…”

Section: Tinjauan Pustakaunclassified

“…Penelitian (Yan, 2013) merupakan penelitian lanjutan dari (Tjhi, 2008) yang memaparkan beberapa permasalahan yang terjadi pada pengelompokan dokumen antara lain ukuran dataset teks yang besar, noise, overlapping, dan ukuran dimensi yang tinggi. Berdasarkan hal tersebut, beberapa teknik dikembangkan seperti (a) Co-clustering yang efektif dalam menangani data dengan dimensi tinggi.…”

Section: Tinjauan Pustakaunclassified

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Pengelompokan Dokumen Menggunakan Dokumen Berlabel dan Tidak Berlabel Dengan Pendekatan Modified Heuristic Fuzzy Co-Clustering

Hayati¹,

Ludviani²,

Rosyadi³

2015

JBI

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Section: Pendahuluanunclassified

Section: Tinjauan Pustakaunclassified

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Pengelompokan Dokumen Menggunakan Dokumen Berlabel dan Tidak Berlabel Dengan Pendekatan Modified Heuristic Fuzzy Co-Clustering

Hayati¹,

Ludviani²,

Rosyadi³

2015

JBI

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show abstract

“…The Table 1 entities into disjoint clusters such that entities of each cluster are similar. Clustering techniques have been emp loyed in many areas such as pattern recognition [3,4], informat ion retrieval [70,71], data mining [72,73], and many mo re. Large nu mber of nature inspired algorith ms is applied on the data clustering problem such as ABC [11], PSO [74], KMH [75], A CO [76], K-means [77], BB-BC [34] etc.…”

Section: Gsa Hybridization and Applicationsmentioning

confidence: 99%

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

Kumar¹,

Sahoo²

2014

IJISA

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Abstract− Natural phenomenon's and swarms behavior are the warm area of research among the researchers. A large number of algorithms have been developed on the account of natural phenomenon's and swarms behavior. These algorithms have been implemented on the various computational problems for the sake of solutions and provided significant results than conventional methods but there is no such algorithm which can be applied for all of the computational problems. In 2009, a new algorithm was developed on the behalf of theory of gravity and was named gravitational search algorithm (GSA) for continuous optimization problems. In short span of time, GSA algorithm gain popularity among researchers and has been applied to large number of problems such as clustering, classification, parameter identification etc. This paper presents the compendious survey on the GSA algorithm and its applications as well as enlightens the applicability of GSA in data clustering & classification.

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“…Clustering algorithms [27][28][29][30][31][32][33][34][35][36][37][38][39] can be applied to text mining to allow the automatic recognition of some sort of structure in the analyzed set of documents. In particular, clustering is designed to discover groups in the set of documents such that the documents within a group are more similar to one another than to documents of other groups.…”

Section: Text Clusteringmentioning

confidence: 99%

Text Clustering for Digital Forensics Analysis

Decherchi

Tacconi²,

Redi

et al. 2009

Advances in Intelligent and Soft Computing

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Abstract:In the last decades digital forensics has become a prominent activity in modern investigations. Seized digital devices can provide precious information and evidences about facts and/or individuals on which the investigational activity is performed. Due to the complexity of this inquiring activity and to the large amount of the data to be analyzed, the choice of appropriate digital tools to support the investigation represents a central concern. In this paper an effective digital text analysis strategy, relying on clusteringbased text mining techniques, is introduced for investigational purposes. The proposed methodology is experimentally applied to the publicly available Enron dataset that well fits a plausible forensics analysis context.

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A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data

Cited by 36 publications

References 15 publications

Pengelompokan Dokumen Menggunakan Dokumen Berlabel dan Tidak Berlabel Dengan Pendekatan Modified Heuristic Fuzzy Co-Clustering

Pengelompokan Dokumen Menggunakan Dokumen Berlabel dan Tidak Berlabel Dengan Pendekatan Modified Heuristic Fuzzy Co-Clustering

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

Text Clustering for Digital Forensics Analysis

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