Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.), 6qo/oohpr+# v# F'#yrqtr 9v+p'o/oor...'#hq#9h#h#Hvvt, 1996, AAAI Press. Home/Search Document Not in Database Summary Related Articles Check |
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Data Quality Mining - Making a Virtue of Necessity - Hipp, Güntzer, Grimmer (2001) (3 citations) (Correct)
....to concentrate on quite special and unfortunately somehow academic questions instead of tackling the crucial problems. What KDD urgently needs for its breakthrough is more business driven research, e.g. by studying real world applications and a better understanding of the KDD process, e.g. (Fayyad et al. 1996; Brachman and Anand, 1996; Wirth and Hipp, 2000) 52 In this paper we want to draw the attention to data quality in the context of KDD. We believe the connection of both fields currently does not get the attention that is implied by its potentials. Basically, there are two aspects of data ....
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996).
Visualising Large Data Sets - Unwin (Correct)
....interactive exploration of subsets using techniques like the hot set selection introduced by Data Desk would be very effective. 3 Visualisation and Data Mining Groups of methods for obtaining information from large data sets have recently attracted attention under the common name of Data Mining. (Fayyad et al. [1996], Mannila [1997] Up till now there has not been much use of visualisation in this field and there is little available of any consequence in Data Mining software. This may be because many methods work with only discrete variables (so that continuous variables have to be discretised) and because ....
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (Ed.). (1996).
Extensibility in Data Mining Systems - Wrobel, Wettschereck, Sommer, Emde (1996) (22 citations) (Correct)
....is presented and discussed. Keywords: data mining, system architecture, extensibility, KEPLER Introduction Data Mining, or Knowledge Discovery in Databases (KDD) aims at finding novel, interesting, and useful information in large real world datasets (Frawley, Piatetsky Shapiro, Matheus 1991; Fayyad, Piatetsky Shapiro, Smyth 1996). Whilebuilding on parent disciplines such as Machine Learning and statistics, the field of data mining differs from these in its stronger orientation to applications on real world databases. In Machine Learning and statistics, the focus of research tends to be mostly on the methods for data ....
....mining to be successful in practice, good system support for the data mining process can be just as crucial as having the right analysis methods. Data mining researchers have responded to this challenge by creating data mining systems that combine support for all steps of the data mining process (Fayyad, Piatetsky Shapiro, Smyth 1996, p. 10) with a fixed selection of analysis algorithms in one integrated environment. ISL s Clementine (Integral Solutions Ltd. 1996) and Lockheed s Recon (Simoudis, Livezey, Kerber 1996) are two commercially 1 In fact, some authors reserve the term KDD to denote the entire process, whereas ....
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Fayyad, U.; Piatetsky-Shapiro, G.; and Smyth, P. 1996.
The Significance of the Missing Data Problem in Knowledge Discovery - Wright (1998) Self-citation (Discovery) (Correct)
.... as a non trivial process which gleans valid and potentially useful information that was previously unknown from stored data [9] Knowledge discovery uses automated learning techniques in an efficient and accurate manner to deal with large amounts of data and provide interesting results to the user [8,9]. There are many current applications of KDD, including the following: Telephone fraud detection [4] Identification of sky objects [7] Targeting high credit risks [10] Identification of faulty network circuits [15] The knowledge discovery process consists of nine tasks as presented in ....
.... of KDD, including the following: Telephone fraud detection [4] Identification of sky objects [7] Targeting high credit risks [10] Identification of faulty network circuits [15] The knowledge discovery process consists of nine tasks as presented in Fayyad, Piatetsky Shapiro, and Smyth [8]. These tasks consist of first developing an understanding of the data domain (1) and selecting appropriate data (2) Then the data must be preprocessed (3) Frequently it is necessary to perform data reduction and projection (4) to translate the data into an understandable form. The KDD approach ....
[Article contains additional citation context not shown here]
U. M. FAYYAD, G. PIATETSKY-SHAPIRO, & P. SMYTH, in Advances in knowledge discovery and data mining, Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, & Ramasamy Uthurusamy, Eds. (AAAI Press/The MIT Press, Menlo Park, CA, 1996), pp. 1-34.
IMIS: Intelligent Management Information System - Käki, Leponiemi, al. (Correct)
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Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.), 6qo/oohpr+# v# F'#yrqtr 9v+p'o/oor...'#hq#9h#h#Hvvt, 1996, AAAI Press.
Currency Exchange Rate Forecasting from News Headlines - Peramunetilleke, Wong (2002) (1 citation) (Correct)
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U.M.Fayyad,G.Piatetsky-Shapiro,P.SmythandR.Uthurusamy,AdvancesinKnowledgeDiscoveryandDataMining, AAAIPress/TheMITPress,pp625,1996.
Comparing two Recommender Algorithms with the Help Of.. - Hahsler (2003) (Correct)
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Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P. In: From Data Mining to Knowledge Discovery: An Overview. MIT Press, Cambridge, MA (1996) 1--36
Clustering using Monte Carlo Cross-Validation - Padhraic Smyth (1996) (28 citations) (Correct)
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Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (eds.), Cambridge, MA: AAAI/MIT Press, pp. 153--180. Chickering, D. M., and Heckerman, D. 1996. `Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network,' MSRTR -96-08 Technical Report, Microsoft Research, Redmond, WA.
CBR as a Framework for Design: Augmenting CBR with other AI.. - Maher (1998) (1 citation) (Correct)
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Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. 1996b.
Learning Symbolic Prototypes - Datta, Kibler (1997) (1 citation) (Correct)
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Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (eds.), Cambridge, MA: AAAI/MIT Press, pp. 153-180. Clark, P. and Boswell, R. (1991). Rule Induction with CN2: some recent improvements. European Working Session on Learning.
Heuristic Search for Model Structure: the Benefits of.. - Elder, IV (1996) (Correct)
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M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy, AAAI/MIT Press. Farlow, S. J. (1984), Ed. Self-Organizing Methods in Modeling: GMDH Type Algorithms. Marcel Dekker.
Graphical Models for Discovering Knowledge - Buntine (1995) (15 citations) (Correct)
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Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R.S. Uthurasamy. MIT Press. Buntine, W.L. 1994. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 2:159--225.
A Multistrategy Learning Approach to Flexible Knowledge.. - Lee, Fischthal, Wnek (Correct)
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Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R, eds.: AAAI Press, Menlo Park. Falkenhainer, B. C., and Michalski, R. S. 1990.
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