The Experts below are selected from a list of 2409 Experts worldwide ranked by ideXlab platform
Gurdal Ertek - One of the best experts on this subject based on the ideXlab platform.
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text mining with Rapidminer
Social Science Research Network, 2013Co-Authors: Gurdal Ertek, Dilek Tapucu, Inanc ArinAbstract:Ertek, G., Tapucu, D., & Arin, I. (2013). Text mining with Rapidminer. Rapidminer: Data Mining Use Cases and Business Analytics Applications, 241.
Heiko Paulheim - One of the best experts on this subject based on the ideXlab platform.
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Extending Rapidminer with data search and integration capabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016Co-Authors: Anna Lisa Gentile, Stephan Kirstein, Heiko Paulheim, Christian BizerAbstract:Analysts are increasingly confronted with the situation that data which they need for a data mining project exists somewhere on the Web or in an organization's intranet but they are unable to find it. The data mining tools that are currently available on the market offer a wide range of powerful data mining methods but hardly support analysts in searching for suitable data as well as in integrating data from multiple sources. This demo shows an extension to Rapidminer, a popular data mining framework, which enables analysts to search for relevant datasets and integrate discovered data with data that they already know. In par-ticular, we support the iterative extension of data tables with additional attributes. We will demonstrate the usage of the extension with a large corpus of tabular data extracted from Wikipedia.
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mining the web of linked data with Rapidminer
Journal of Web Semantics, 2015Co-Authors: Petar Ristoski, Christian Bizer, Heiko PaulheimAbstract:Lots of data from different domains are published as Linked Open Data (LOD). While there are quite a few browsers for such data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this system paper, we introduce the Rapidminer Linked Open Data extension. The extension hooks into the powerful data mining and analysis platform Rapidminer, and offers operators for accessing Linked Open Data in Rapidminer, allowing for using it in sophisticated data analysis workflows without the need for expert knowledge in SPARQL or RDF. The extension allows for autonomously exploring the Web of Data by following links, thereby discovering relevant datasets on the fly, as well as for integrating overlapping data found in different datasets. As an example, we show how statistical data from the World Bank on scientific publications, published as an RDF data cube, can be automatically linked to further datasets and analyzed using additional background knowledge from ten different LOD datasets.
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mining the web of linked data with Rapidminer
Social Science Research Network, 2015Co-Authors: Petar Ristoski, Christian Bizer, Heiko PaulheimAbstract:Lots of data from different domains is published as Linked Open Data (LOD). While there are quite a few browsers for such data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this system paper, we introduce the Rapidminer Linked Open Data extension. The extension hooks into the powerful data mining and analysis platform Rapidminer, and offers operators for accessing Linked Open Data in Rapidminer, allowing for using it in sophisticated data analysis workflows without the need for expert knowledge in SPARQL or RDF. The extension allows for autonomously exploring the Web of Data by following links, thereby discovering relevant datasets on the fly, as well as for integrating overlapping data ound in different datasets. As an example, we show how statistical data from the World Bank on scientific publications, published as an RDF data cube, can be automatically linked to further datasets and analyzed using additional background knowledge from ten different LOD datasets.
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Exploiting Linked Open Data as Background Knowledge in Data Mining
CEUR workshop proceedings DMoLD 2013 : Proceedings of the International Workshop on Data Mining on Linked Data with Linked Data Mining Challenge collo, 2013Co-Authors: Heiko PaulheimAbstract:Abstract. Many data mining problems can be solved better if they are augmented with additional background knowledge. This paper discusses a framework of adding background knowledge from Linked Open Data to a given data mining problem in a fully automatic, unsupervised manner. It introduces the FeGeLOD framework and its latest implementation, the Rapidminer Linked Open Data extension. We show the use of the approach in different problem domains and discuss current research directions.
Inanc Arin - One of the best experts on this subject based on the ideXlab platform.
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text mining with Rapidminer
Social Science Research Network, 2013Co-Authors: Gurdal Ertek, Dilek Tapucu, Inanc ArinAbstract:Ertek, G., Tapucu, D., & Arin, I. (2013). Text mining with Rapidminer. Rapidminer: Data Mining Use Cases and Business Analytics Applications, 241.
Čišecký Roman - One of the best experts on this subject based on the ideXlab platform.
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Digital Image Noise Reduction Methods
Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012Co-Authors: Čišecký RomanAbstract:Diplomová práca sa zaoberá metódami na odstránenenie šumu z digitálnych obrazov. V teoretickej časti sú vysvetlené niektoré základné pojmy súvisiace so spracovaním obrazu, obrazovým šumom, rozdelenie šumu a kritéria na určovanie kvality odšumovacieho procesu. Ďalej sú v práci popísané jednotlivé metódy na odstránenie šumu, spomenuté sú ich výhody a nevýhody. Praktická časť sa zaoberá vlastnou implementáciou vybraných metód v jazyku Java, v aplikácii Rapidminer. V závere sú porovnané výsledky dosiahnuté jednotlivými metódami.The master's thesis is concerned with digital image denoising methods. The theoretical part explains some elementary terms related to image processing, image noise, categorization of noise and quality determining criteria of denoising process. There are also particular denoising methods described, mentioning their advantages and disadvantages in this paper. The practical part deals with an implementation of the selected denoising methods in a Java, in the environment of application Rapidminer. In conclusion, the results obtained by different methods are compared.
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Digital Image Noise Reduction Methods
Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012Co-Authors: Čišecký RomanAbstract:The master's thesis is concerned with digital image denoising methods. The theoretical part explains some elementary terms related to image processing, image noise, categorization of noise and quality determining criteria of denoising process. There are also particular denoising methods described, mentioning their advantages and disadvantages in this paper. The practical part deals with an implementation of the selected denoising methods in a Java, in the environment of application Rapidminer. In conclusion, the results obtained by different methods are compared
Lauw Christian - One of the best experts on this subject based on the ideXlab platform.
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Penerapan Aplikasi Rapidminer Untuk Prediksi Nilai Tukar Rupiah Terhadap US Dollar Dengan Metode Regresi Linier
'Perpustakaan Universitas Andalas', 2021Co-Authors: Prasetyo, Vincentius Riandaru, Lazuardi Hamzah, Mulyono, Aldo Adhi, Lauw ChristianAbstract:Kurs adalah sebuah nilai mata uang suatu negara terhadap mata uang lain. Oleh karena itu, kurs memiliki dua komponen utama yaitu mata uang domestik, dan mata uang asing. Mata uang asing yang sering digunakan sebagai patokan nilai tukar adalah US Dollar. Di berbagai negara termasuk Indonesia, nilai tukar mata uang terhadap US Dollar sangat mempengaruhi perekonomian yang berjalan, terutama harga jual suatu barang. Selain itu, nilai tukar mata uang juga berpengaruh terhadap keputusan seseorang untuk berinvestasi, baik saham, emas, atau yang lain. Penelitian ini mencoba memprediksi nilai tukar rupiah terhadap US Dollar dengan memanfaatkan aplikasi Rapidminer. Aplikasi tersebut merupakan aplikasi freeware yang didalamnya terdapat berbagai macam metode pengolahan data yang siap untuk digunakan secara mudah. Penelitian ini menerapkan metode linear regression yang terdapat pada aplikasi Rapidminer. Metode tersebut akan mengolah data-data yang sudah ada sebelumnya untuk membentuk suatu persamaan yang akan digunakan untuk prediksi nilai tukar rupiah terhadap US Dollar. Atribut yang digunakan untuk melakukan prediksi adalah nilai pembukaan, perubahan, tertinggi, dan terendah dari nilai tukar rupiah terhadap US Dollar. Data yang digunakan pada penelitian ini berasal dari situs investing.com. Dari hasil pengujian yang dilakukan, didapatkan akurasi metode linear regression sebesar 95% dengan nilai threshold adalah 30 rupiah. Selain itu, nilai root mean squared error yang didapatkan sebesar 14,951
