An Efficient Association Rule Based Clustering of XML Documents

Muralidhar, A.; Pattabiraman, V.

doi:10.1016/j.procs.2015.04.024

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…There are other studies like [41] in web mining with a concentration of XML document clustering based on content to find the more interesting XML documents on the web. Corpus like Wikipedia, which exist on the web, are huge and classified as big data collection.…”

Section: Xml Document Clustering Based On Only Content Featuresmentioning

confidence: 99%

“…Finding similar features for such a collection takes lots of time. In order to solve this problem, Muralidhar et al [41] proposed a hybrid approach that first finds frequent XML documents through the Apriori algorithm of Association rule mining and then clusters XML documents by the popular K-means clustering algorithm. This approach can be useful for clustering of XML documents in the web environment and improving retrieving information in the web, but ignoring infrequent documents is not appropriate for an static XML document collection like DL because each document in the collection may meet the need of a user.…”

Section: Xml Document Clustering Based On Only Content Featuresmentioning

confidence: 99%

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XML Clustering Framework Based on Document Content and Structure in a Heterogeneous Digital Library

Samadi,

(Corresponding Author)

2023

MJCS

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As textually published information is increasing in digital libraries, efficient retrieval methods are required. Textual documents in a digital library are available in various structures and contents. It is possible to represent these documents with hierarchical levels of granularity when these are organized in XML structure to improve precision by focused retrieval. By this means, contextual elements of each document can be retrieved from a known structure. One solution for retrieving these elements is clustering from a combination of Content and Structural similarities. To achieve this, a novel two-level clustering framework based on Content and Structure is proposed. The framework decomposes a document into meaningful structural units and analyzes all its rich text in its own structure. The quality of the proposed framework was experimented on a heterogeneous XML document collection, having varieties of data sources, structures, and content, be represented as a sample of a real digital library. This collection was made with capabilities to test all of our objectives. The clustering results were evaluated by the Entropy criterion. Finally, the Content and Structure clustering was compared with the usual clustering based on the Content Only to prove the efficacy of considering structural features against the existing Content Only methods in the retrieval process. The total Entropy results of the two-level Content and Structural clustering are almost twice better than the Content Only clustering approach. Consequently, the proposed framework has the ability to improve Information Retrieval systems from two points of view: i) considering the structural aspect of text-rich documents in the retrieval process, and ii) replacing the document-level retrieval with the element-level retrieval.

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Section: Xml Document Clustering Based On Only Content Featuresmentioning

confidence: 99%

Section: Xml Document Clustering Based On Only Content Featuresmentioning

confidence: 99%

XML Clustering Framework Based on Document Content and Structure in a Heterogeneous Digital Library

Samadi,

(Corresponding Author)

2023

MJCS

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“…[1] Sistem Rekomendasi (SR) yang baik memberikan rekomendasi produk yang mungkin menarik bagi pengguna yang tidak berencana membeli menjadi membeli lebih banyak lagi dan akan meningkatkan penjualan [2]. SR dapat menghasilkan rekomendasi dengan berbagai cara dan menggunakan berbagai macam metode, salah satunya adalah memanfaatkan tumpukan kasus lama atau tumpukan data transaksi lama yang dapat menghasilkan informasi dengan metode ARM [3]. ARM akan menghasilkan pola aturan dari data transaksi lama tersebut dan aturan yang dihasilkan dapat dimanfaatkan untuk rekomendasi [4].…”

Section: Pendahuluanunclassified

Aturan Rekomendasi Barang Menggunakan Multi Level Association Rules Mining (Ml-Arm)

Tyas

2018

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Sistem rekomendasi dapat menghasilkan rekomendasi dengan berbagai cara dan menggunakan berbagai macam metode, salah satunya adalah memanfaatkan tumpukan kasus lama atau tumpukan data transaksi lama yang dapat menghasilkan informasi atau aturan dengan metode Association Rules Mining(ARM). Aturan terbentuk dengan metode multi level ARM dan menghasilkan 5 aturan yang akan dicocokkan dengan masukan pengguna. Saat aturan ditemukan cocok maka consequent dari aturan tersebut akan dijadikan hasil rekomendasi. Hasil pengujian dari aturan yang terbentuk memiliki nilai akurasi 94,12% dan nilai precision, recall dan F-measure untuk sistem rekomendasi ini pada proses rekomendasi dengan aturan yaitu berturut 0,475; 0,513 dan 0,25.

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