A similarity measure on tree structured business data

In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes&rsquo; concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system.<br /

Pendeteksian Dokumen Plagiarisme dengan Menggunakan Metode Weight Tree

Sistem pengelolaan dokumen plagiarisme masih ada yang dilakukan secara manual yaitu dengan mengecek satu persatu sehingga membutuhkan waktu yang lama dan kurang efektif. Salah satu algoritma yang dapat digunakan untuk pendeteksian dokumen plagiarisme adalah algoritma Weight Tree yaitu sebuah metode untuk melakukan klasifikasi kemiripan dokumen berdasarkan bobot dari dokumen. Tujuan penelitian ini adalah untuk membangun sebuah sistem pendeteksian kemiripan dari dua dokumen teks yang berbeda untuk jenis dokumen teks berbahasa indonesia dengan format file dokumen yaitu: doc, docx, pdf, rtf. Tahapan yang dilakukan pada penelitian ini terdiri dari pengumpulan data, perancangan sistem, pembuatan aplikasi dan pengujian terhadap aplikasi. Hasil pengujian sistem dapat dikategorikan sebagai sistem pendeteksian atau pengetesan kemiripan dokumen. Pada pengujian sistem ini, penulis yang mengkategorikan dokumen tersebut sebagai dokumen plagiat berdasarkan persentase kemiripan. Nilai rata-rata persentase kemiripan dalam pengujian sistem ini adalah 71,60%. Sistem yang di bangun ini berhasil dengan tingkat keakuratan mencapai 90%. Algoritma Weight Tree yang diterapkan pada sistem ini terbukti mampu mengidentifikasi dengan baik kemiripan dokumen plagiarisme.</p

Similarity measure models and algorithms for hierarchical cases

Many business situations such as events, products and services, are often described in a hierarchical structure. When we use case-based reasoning (CBR) techniques to support business decision-making, we require a hierarchical-CBR technique which can effectively compare and measure similarity between two hierarchical cases. This study first defines hierarchical case trees (HC-trees) and discusses related features. It then develops a similarity evaluation model which takes into account all the information on nodes' structures, concepts, weights, and values in order to comprehensively compare two hierarchical case trees. A similarity measure algorithm is proposed which includes a node concept correspondence degree computation algorithm and a maximum correspondence tree mapping construction algorithm, for HC-trees. We provide two illustrative examples to demonstrate the effectiveness of the proposed hierarchical case similarity evaluation model and algorithms, and possible applications in CBR systems. © 2011 Elsevier Ltd. All rights reserved

PENERAPAN ALGORITMA WEIGHTED TREE SIMILARITY UNTUK PENCARIAN SEMANTIK

Full-text search and metadata-enabled search have weakness in the precision of the searched article. This research offers weighted tree similarity algorithm combined with cosine similarity method to count similarity in semantic search. In this method metadata is constructed based on the tree of labelled node, labelled and weighted branch. The structure of tree metadata is constructed based on semantic information like taxonomi, ontologi, preference, synonim, homonym and stemming. From testing result, the precision of search using weighted tree similarity algorithm is better that full-text search and metadata-enabled search

A Weighted-Tree Simplicity Algorithm for Similarity Matching of Partial Product Descriptions

Our weighted-tree similarity algorithm matches buyers and sellers in e-Business environments. We use arc-labeled, arc-weighted trees to represent the products (or services) sought/offered by buyers/sellers. Partial product descriptions can be represented via subtrees missing in either or both of the trees. In order to take into account the effect of a missing subtree on the similarity between two trees, our algorithm uses a (complexity or) simplicity measure. Besides tree size (breadth and depth), arc weights are taken into account by our tree simplicity algorithm. This paper formalizes our buyer/seller trees and analyzes the properties of the implemented tree simplicity measure. We discuss how this measure captures business intuitions, give computational results on the simplicity of balanced k-ary trees, and show that they conform to the theoretical analysis.Notre algorithme de similitude d'arborescences \ue0 pond\ue9ration correspond \ue0 celui des acheteurs et des vendeurs des environnements d'affaires \ue9lectroniques. Nous utilisons des arborescences \ue9tiquet\ue9es et pond\ue9r\ue9es par arc pour repr\ue9senter les produits (ou les services) recherch\ue9s/offerts par les acheteurs/vendeurs. Des descriptions partielles de produits peuvent \ueatre repr\ue9sent\ue9es \ue0 l'aide de sous-arborescences manquantes dans l'une des arborescences ou les deux. Afin de prendre en compte l'effet d'une arborescence manquante sur la similitude entre les deux arborescences, notre algorithme utilise une mesure de simplicit\ue9 (ou de complexit\ue9). Outre la taille de l'arborescence (\ue9tendue et profondeur), les poids d'arc sont pris en compte par notre algorithme de simplicit\ue9 d'arborescence. Ce document officialise les arborescences des acheteurs/vendeurs et analyse les propri\ue9t\ue9 de la mesure de simplicit\ue9 d'arborescence implant\ue9e. Nous discutons de la fa\ue7on dont cette mesure saisit les intuitions commerciales, fournit des r\ue9sultats informatiques concernant la simplicit\ue9 des arborescences \uab k-ary \ubb \ue9quilibr\ue9es et montre qu'ils sont conformes \ue0 l'analyse th\ue9orique.NRC publication: Ye

A WEIGHTED-TREE SIMPLICITY ALGORITHM FOR SIMILARITY MATCHING OF PARTIAL PRODUCT DESCRIPTIONS

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A Weighted-Tree Simplicity Algorithm for Similarity Matching of Partial Product Descriptions

Our weighted-tree similarity algorithm matches buyers and sellers in e-Business environments. We use arc-labeled, arc-weighted trees to represent the products (or services) sought/offered by buyers/sellers. Partial product descriptions can be represented via subtrees missing in either or both of the trees. In order to take into account the effect of a missing subtree on the similarity between two trees, our algorithm uses a (complexity or) simplicity measure. Besides tree size (breadth and depth), arc weights are taken into account by our tree simplicity algorithm. This paper formalizes our buyer/seller trees and analyzes the properties of the implemented tree simplicity measure. We discuss how this measure captures business intuitions, give computational results on the simplicity of balanced k-ary trees, and show that they conform to the theoretical analysis.Notre algorithme de similitude d'arborescences \ue0 pond\ue9ration correspond \ue0 celui des acheteurs et des vendeurs des environnements d'affaires \ue9lectroniques. Nous utilisons des arborescences \ue9tiquet\ue9es et pond\ue9r\ue9es par arc pour repr\ue9senter les produits (ou les services) recherch\ue9s/offerts par les acheteurs/vendeurs. Des descriptions partielles de produits peuvent \ueatre repr\ue9sent\ue9es \ue0 l'aide de sous-arborescences manquantes dans l'une des arborescences ou les deux. Afin de prendre en compte l'effet d'une arborescence manquante sur la similitude entre les deux arborescences, notre algorithme utilise une mesure de simplicit\ue9 (ou de complexit\ue9). Outre la taille de l'arborescence (\ue9tendue et profondeur), les poids d'arc sont pris en compte par notre algorithme de simplicit\ue9 d'arborescence. Ce document officialise les arborescences des acheteurs/vendeurs et analyse les propri\ue9t\ue9 de la mesure de simplicit\ue9 d'arborescence implant\ue9e. Nous discutons de la fa\ue7on dont cette mesure saisit les intuitions commerciales, fournit des r\ue9sultats informatiques concernant la simplicit\ue9 des arborescences \uab k-ary \ubb \ue9quilibr\ue9es et montre qu'ils sont conformes \ue0 l'analyse th\ue9orique.NRC publication: Ye

H.: A Weighted-Tree Simplicity Algorithm for Similarity Matching of Partial Product Descriptions

Our weighted-tree similarity algorithm matches buyers and sellers in e-Business environments. We use arc-labeled, arc-weighted trees to represent the products (or services) sought/offered by buyers/sellers. Partial product descriptions can be represented via subtrees missing in either or both of the trees. In order to take into account the effect of a missing subtree on the similarity between two trees, our algorithm uses a (complexity or) simplicity measure. Besides tree size (breadth and depth), arc weights are taken into account by our tree simplicity algorithm. This paper formalizes our buyer/seller trees and analyzes the properties of the implemented tree simplicity measure. We discuss how this measure captures business intuitions, give computational results on the simplicity of balanced k-ary trees, and show that they conform to the theoretical analysis. Key Words Arc-labeled and arc-weighted tree, tree similarity, tree simplicity, balanced k-ary trees, e-Business, buyer and seller trees. 1