yosnex 2009

7 June 2012

Tesis 12

Filed under: Kuliah 2.2 — yosnex @ 7:26 am

Berikut definisi Data Mining dari berbagai Jurnal Internasional. Maaf, Jurnal aslinya supaya di cari sendiri, tidak di sharing. Khawatir dikira Penjiplakan alias Plagiat, atau dituduh sebagai Pendukung Plagiat/Penjiplakan. Bila ada Dosen atau Dosen Anda yang MELARANG mengutip dari BLOG, ya kutipan di sini jangan dikutip, krn Blog itu tidak 100% ilmiah & kredible. Yang 100% ilmiah itu bila punya Jurnal sendiri.

Dengan format = Referensi : Definisi

Data Mining Applications:Promise and Challenges, Rukshan Athauda, Menik Tissera and Chandrika Fernando, Data Mining and Knowledge Discovery in Real Life Applications, Julio Ponce, Adem Karahoca, In-Teh, 2009, 201 :

  1. “Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Gartner Group, 1995).

  2. “Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” (Hand et al., 2001).

  3. “Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large data bases” (Cabena et al., 1998).

  4. “The extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data” (Han & Kamber, 2001).

Data Mining Applications:Promise and Challenges, Rukshan Athauda, Menik Tissera and Chandrika Fernando, Data Mining and Knowledge Discovery in Real Life Applications, Julio Ponce, Adem Karahoca, In-Teh, 2009/244 :

  1. Frawley et al. (1991) declared that data mining is actually a process of discovering of nonobvious, unprecedented, and potentially useful information.

  2. Curt (1995) defined data mining as a database transformation process, in which the information is transformed from unorganized vocabulary and numbers to organized data, and later turned into knowledge from which a decision can be made.

  3. Fayyad et al. (1996) stated that data mining is an uncomplicated process of discovering valid, brand new, potentially useful, and comprehensive patterns from data.

  4. Hui and Jha (2000) defined data mining as an analysis of automation and semiautomation for the discovery of meaningful relationships and rules from a large amount of data in a database.

  5. Hand et al. (2000) stated that data mining is a process that discovers interesting and valuable information from a database.

  6. Berson et al. (2001) argued that the appeal of data mining lies in its forecasting competence instead of merely in its ability to trace back.

To summarize the foregoing definitions, data mining is a process of obtaining knowledge.

Prediction with the SVM Using Test Point Margins, Sureyya Ozogur-Akyuz, Zakria Hussain and John Shawe-Taylor, Data Mining, Special Issue in Annals of Information Systems, Robert Stahlbock, Sven F. Crone, Stefan Lessmann, Springer, Nov. 2009, 147 :

Data mining is the process of analyzing data to gather useful information or structure. It has been described as the science of extracting useful information from large data sets or databases [D. Hand, H. Mannila, and P. Smyth. An introduction to Support Vector Machines. MIT Press, Cambridge, MA, 2001.] or the nontrivial extraction ofimplicit, previously unknown, and potentially useful information from data [W. Frawley, G. Piatetsky-Shapiro, and C. Matheus. Knowledge discovery in databases: An overview. In AI Magazine, pages 213–228, 1992.].

Data Mining and Computational Intelligence. Physica Verlag, Data Mining In Time Series Databases, Mark Last, Abraham Kandel, Horst Bunke, World Scientific Publishing, 2004, 128 :

Data mining is generally concerned with the detection and extraction of meaningful patterns and rules from large amounts of data [Fayyad, U., Piatetsky-Sharpiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press.] & [Kandel, A., Last, M., and Bunke, H. (2001).

On the Use of Evolutionary Algorithms in Data Mining, Erick Cantú-Paz and Chandrika Kamath, Center for Applied Scientific Computing Lawrence Livermore National Laboratory, USA, Data Mining, A Heuristic Approach, Hussein Aly Abbass, Ruhul Amin Sarker, Charles S. Newton, Idea Group Publishing, 2002, 49 :

Data mining is a process concerned with uncovering patterns, associations, anomalies and statistically significant structures in data (Fayyad et al., 1996). It typically refers to the case where the data is too large or too complex to allow either a manual analysis or analysis by means of simple queries. Data mining consists of two main steps, data pre-processing, during which relevant high-level features or attributes are extracted from the low level data, and pattern recognition, in which a pattern in the data is recognized using these features.

On the Use of Evolutionary Algorithms in Data Mining, Erick Cantú-Paz and Chandrika Kamath, Center for Applied Scientific Computing Lawrence Livermore National Laboratory, USA, Data Mining, A Heuristic Approach, Hussein Aly Abbass, Ruhul Amin Sarker, Charles S. Newton, Idea Group Publishing, 2002, 50 :

While there is some debate about the exact definition of data mining (Kamath,2001), most practitioners and proponents agree that data mining is a multidisciplinary field, borrowing ideas from machine learning and artificial intelligence, statistics, high performance computing, signal and image processing, mathematical optimization, pattern recognition, etc.

Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types, Gregor Stiglic, Nawaz Khan, and Peter Kokol, Data Mining, Foundations and Practice, Tsau Young Lin, Ying Xie, Anita Wasilewska, Churn-Jung Liau, Springer, 2008, 340 :

Data mining is the process of autonomously extracting useful information or knowledge from large datasets.

Wavelet Methods in Data Mining, Tao Li1, Sheng Ma, and Mitsunori Ogihara, Data Mining and Knowledge Discovery Handbook, Oded Maimon, Lior Rokach, Springer, 2nd, 2010, 553  :

Data Mining is a process of automatically extracting novel, useful, and understandable patterns from a large collection of data. Over the past decade this area has become significant both in academia and in industry.Wavelet theory could naturally play an important role in Data Mining because wavelets could provide data presentations that enable efficient and accurate mining process and they can also could be incorporated at the kernel for many algorithms. Although standard wavelet applications are mainly on data with temporal/spatial localities (e.g., time series data, stream data, and image data), wavelets have also been successfully applied to various Data Mining domains.

Quality Assessment Approaches in Data Mining, Maria Halkidi and Michalis Vazirgiannis, Data Mining and Knowledge Discovery Handbook, Oded Maimon, Lior Rokach, Springer, 2nd, 2010, 613 :

Data Mining is mainly concerned with methodologies for extracting patterns from large data repositories. There are many Data Mining methods which accomplishing a limited set of tasks produces a particular enumeration of patterns over data sets. The main tasks of Data Mining which have already been discussed in previous sections are: i) Clustering, ii) Classification, iii) Association Rule Extraction, iv)Time Series, v) Regression, and vi) Summarization.

Data Stream Mining, Mohamed Medhat Gaber, Arkady Zaslavsky, and Shonali Krishnaswamy, Data Mining and Knowledge Discovery Handbook, Oded Maimon, Lior Rokach, Springer, 2nd, 2010, 759 :

Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data repositories. It is an interdisciplinary field of study that has its roots in databases, statistics, machine learning, and data visualization. Data mining has emerged as a direct outcome of the data explosion that resulted from the success in database and data warehousing technologies over the past two decades (Fayyad, 1997,Fayyad, 1998,Kantardzic, 2003).

Organizational Data Mining, Hamid R. Nemati and Christopher D. Barko, Data Mining and Knowledge Discovery Handbook, Oded Maimon, Lior Rokach, Springer, 2nd, 2010, 1043 :

Data Mining is the process of discovering and interpreting previously unknown patterns in  databases

Commercial Data Mining Software, Qingyu Zhang and Richard S. Segall, Data Mining and Knowledge Discovery Handbook, Oded Maimon, Lior Rokach, Springer, 2nd, 2010, 1245 :

Data mining is defined by the Data Intelligence Group (1995) as the extraction of hidden predictive information form large databases.

According to them, “data mining tools scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.” According to StatSoft (2006), algorithms are operations or procedures that will produce a particular outcome with a completely defined set of steps or operations.

Automated Anomaly Detection / Brad Morantz, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 108 :

Data mining is an exploratory process looking for as yet unknown patterns (Westphal & Blaxton, 1998).

Automated Anomaly Detection / Brad Morantz, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 112 :

 Data mining is an exploratory process to see what is in the data and what patterns can be found.

Data Mining in Human Resources / Marvin D. Troutt and Lori K. Long, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 293 :

Data mining is essentially the extracting of knowledge based on patterns of data in very large databases and is an analytical technique that may become a valuable tool for HR professionals.

 Data Mining in the Soft Computing Paradigm / Pradip Kumar Bala, Shamik Sural, and Rabindra Nath Banerjee, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 303 :

Data mining is a set of tools, techniques and methods that can be used to find new, hidden or unexpected patterns from a large volume of data typically stored in a data warehouse.

Distributed Association Rule Mining / Mafruz Zaman Ashrafi, David Taniar, and Kate A. Smith, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 434 :

Data mining is an iterative and interactive process that explores and analyzes voluminous digital data to discover valid, novel, and meaningful patterns (Mohammed, 1999).

Ethics of Data Mining / Jack Cook, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 485 :

Data mining is the process of discovering and interpreting meaningful, previously hidden patterns in the data. It is not a set of descriptive statistics.

Homeland Security Data Mining and Link Analysis / Bhavani Thuraisingham, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 597 :

Data mining is the process of posing various queries and extracting useful information, patterns, and trends often previously unknown from large quantities of data possibly stored in databases.

Locally Adaptive Techniques for Pattern Classification / Carlotta Domeniconi and Dimitrios Gunopulos, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 715 :

Data mining is a knowledge-discovery process whose aim is to discover unknown relationships and/or patterns from a large set of data, from which it is possible to predict future outcomes.

Mining for Web-Enabled E-Business Applications / Richi Nayak, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 816 :

Data mining is the process of searching the trends, clusters, valuable links and anomalies in the entire data. The process benefits from the availability of large amount of data with rich description.

Mining for Web-Enabled E-Business Applications / Richi Nayak, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 820 :

Data Mining (DM) or Knowledge Discovery in Databases : The extraction of interesting, meaningful, implicit, previously unknown, valid and actionable information from a pool of data sources.

Mining Frequent Patterns via Pattern Decomposition / Qinghua Zou and Wesley Chu, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 821 :

A fundamental problem in data mining is the process of finding frequent itemsets (FI) in a large dataset that enable essential data-mining tasks, such as discovering association rules, mining data correlations, and mining sequential patterns.

Negative Association Rules in Data Mining / Olena Daly and David Taniar, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006, 890 :

Data Mining is a process of discovering new, unexpected, valuable patterns from existing databases (Chen, Han & Yu, 1996; Fayyad et. al., 1996; Frawley, Piatetsky-Shapiro & Matheus, 1991; Savasere, Omiecinski & Navathe, 1995).

Organizational Data Mining / Hamid R. Nemati and Christopher D. Barko, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006., 923 :

Data mining is the process of discovering and interpreting previously unknown patterns in databases. It is a powerful technology that converts data into information and potentially actionable knowledge.

Synthesis with Data Warehouse Applications and Utilities / Hakikur Rahman, Encyclopedia of Data Warehousing and Mining, John Wang, Idea Group Inc., 2006., 1124 :

Data mining is the search for patterns and structure in large data sets, and the discovery of information may not be present explicitly in the data. However, one of the most difficult problems in data mining is to concretely define the classes of patterns that may be of interest. Riedel, et al. (2000, p. 3) stated that “the major obstacles to starting a data mining project within an organization is the high initial cost of purchasing the necessary hardware”.

The end …………

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