Top 10 algorithms in data mining

Wu, Xindong; Kumar, Vipin; Quinlan, J. R.; Ghosh, Joydeep; Yang, Qiang; Motoda, Hiroshi; McLachlan, Geoffrey J.; Ng, Shu‐Kay; Liu, Bing; Yu, Philip S.; Zhou, Zhi‐Hua; Steinbach, Michael; Hand, David J.; Steinberg, Dan

doi:10.1007/s10115-007-0114-2

Cited by 4,571 publications

(2,236 citation statements)

References 75 publications

Supporting

Mentioning

2,048

Contrasting

Unclassified

159

Order By: Relevance

“…Decision trees (i.e. a kind of flowchart for the classification of new data) are a common way of performing and visualizing that classification [7]. Decision trees can be generated by many different algorithms, though the most well known are CLS, ID3, C4.5, C5.0, and CART.…”

Section: Data Sciencementioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

209

108

View full text Add to dashboard Cite

The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

show abstract

Section: Data Sciencementioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

209

108

View full text Add to dashboard Cite

show abstract

“…In association rule mining, the common words are regarded as frequent itemsets. OCFI builds FP-tree structure [18]- [20] and uses FP-growth method to find out all common words (closed frequent itemset) from document set. Fig.4 is a document set that contains twelve documents and there topics, and Fig.…”

Section: The Proposed Approachmentioning

confidence: 99%

A Hierarchical Document Clustering Approach with Frequent Itemsets

Lee¹,

Hsu²,

Chen³

2017

IJET

View full text Add to dashboard Cite

Abstract-In order to effectively retrieve required information from the large amount of information collected from the Internet, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document of collected information and require special handling for high dimensionality and high volume. We propose the OCFI (Ontology and Closed Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering. OCFI uses common words to cluster documents and builds hierarchical topic tree. In addition, OCFI utilizes ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results reveal that our method is more effective than the well-known document clustering algorithms. The clustering results can be used in the personalized search service to assist users to obtain the information they need.Index Terms-OCFI, documents clustering, ontology, closed frequent itemsets. I. INTRODUCTIONDue to the popularity of the Internet, a large number of documents, reports, e-mails, and web pages cause the information overload problem. Many enterprises spend lots of manpower on organizing these unstructured data into a logical structure for later use. In order to save the manpower and find interesting knowledge effectively, text mining becomes more and more important. information from text, text mining usually involves the process of parsing the input text, mining valuable information through the analysis models, and finally evaluating and interpreting the output. However, most of the text mining algorithms do not have enough capability to solve the problems from text mining, because many methods are modifications of traditional data mining algorithms that were originally designed for relational database. Therefore, traditional clustering algorithms become impractical in real-world document clustering which requires special handling for high dimensionality, high volume, and ease of browsing.Fung proposed Frequent Itemset-based Hierarchical Clustering (FIHC) [11] method to solve the problems from traditional algorithms. FIHC is a hierarchical clustering method developed for document clustering. Clustering or cluster analysis is the task of assigning a set of objects into groups, called clusters, so that the objects in the same cluster are more similar to each other than to those in other clusters. Clustering is a main topic of data mining algorithm, and a common technique for statistical data analysis used in many fields, including machine learning, image analysis, information retrieval, and bioinformatics.A major breakthrough of FIHC is that the clustering algorithm utilizes an important notion, frequent items...

show abstract

“…Algoritme ini merupakan pengembangan dari Algoritme ID3. Bahkan dalam suatu survey paper tahun 2008, C.45 termasuk 10 Top algorithm in data mining [8]. Algoritme ini pertama kali diperkenalkan tahun 1993 oleh Qunlan [9].…”

Section: E Algoritma C45unclassified

Penggunaan Fitur Kimia Fisik dan Posisi Atom untuk Prediksi Struktur Sekunder Protein

Haryanto¹,

Budiman²

2015

JEPIN

View full text Add to dashboard Cite

133Abstrak-Prediksi struktur sekunder protein adalah salah satu masalah yang sudah lama dibahas dalam bidang bioinformatika. Berbagai metode telah diterapkan namun masalah akurasi belum mencapai hasil yang maksimal. Penelitian ini dilakukan untuk membangun suatu model prediksi struktur sekunder protein dengan menggunakan decision tree dengan fitur kimiafisik dan posisi atom. Penentuan setiap kelas dalam proses klasifikasi struktur sekunder protein dalam penelitian ini berdasarkan pembelajaran terhadap pola masukan dalam proses pelatihan. Data diperoleh dari Protein Data Bank (PDB). Sementara informasi struktur sekunder protein diperoleh melalui alamat DSSP. Sejumlah 75809 alpha-helix (H), 41785 untuk bheta-sheet (E), dan 80346 untuk coil (C) digunakan sebagai data set pada penelitian ini. Pola masukan diperoleh melalui proses sliding window dari sekuen asam amino dengan ekstraksi fitur kimiafisik dan posisi atom. Model prediksi dengan cross validation tanpa fitur posisi atom menghasilkan nilai akurasi 90.49%, sedangkan untuk pengujian dengan unknown data akurasinya menurun menjadi 51.29%. Akurasi menggunakan fitur posisi atom sebesar 90.17% dengan cross validation dan 50.83% jika diujikan pada unknown data.Kata Kunci-asam amino, decision tree, kimiafisik, prediksi struktur protein, posisi atom I. PENDAHULUAN Protein mempunyai struktur yang sangat kompleks. Protein ini terbentuk dari urutan asam amino dengan karakteristik berbeda-beda. Secara hierarki, struktur protein dapat dikelompokkan menjadi 4 struktur utama yaitu struktur primer, struktur sekunder, struktur tersier dan struktur kuartener [1]. Struktur primer merupakan urutan asam amino yang dihasilkan dari ikatan peptida. Struktur sekunder adalah rangkaian asam amino yang membentuk struktur membelit, melingkar, dan melipat. Bentuk struktur ini dikelompokkan menjadi struktur alpha-helix (H), beetha-sheet (B), dan coil (C). Adapun struktur tersier merupakan gabungan dari berbagai struktur sekunder yang terjadi setelah proses pelipatan (folding).Peranan protein ini terlihat jelas setelah rangkaian asam amino melakukan pelipatan dalam bentuk 3 dimensi (3D) sebagai struktur tersier. Namun struktur tersier (3D) tersebut ditentukan oleh struktur sebelumnya baik struktur primer maupun struktur sekundernya. Oleh karena itu penentuan struktur sekunder protein menjadi salah satu kajian yang banyak dilakukan di bidang bioinformatika.Untuk Kedua Penelitian sebelumnya memanfaatkan teknik klasifikasi dan ekstraksi ciri yang berbeda. Berdasarkan penelitian tersebut, penelitian ini diajukan untuk membuat prediksi struktur sekunder protein dengan menerapkan algoritme C4.5 dengan ekstraksi fitur kimiafisik dan posisi atom. Kemudian penelitian ini juga akan menentukan sliding window yang optimal agar didapat akurasi yang baik. Hasil dari klasifikasi akan membentuk sebuah model untuk memprediksi struktur sekunder protein.

show abstract

Top 10 algorithms in data mining

Cited by 4,571 publications

References 75 publications

Data science for building energy management: A review

Data science for building energy management: A review

A Hierarchical Document Clustering Approach with Frequent Itemsets

Penggunaan Fitur Kimia Fisik dan Posisi Atom untuk Prediksi Struktur Sekunder Protein

Contact Info

Product

Resources

About