U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Adavances in Knowledge Discovery and Data Mining, pages 1--34. AAAI/MIT Press, 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....its interestingness. Our experimental results show that the ranks assigned by the four interestingness measures are highly correlated. 1 Introduction Knowledge discovery from databases (KDD) is the nontrivial process of identifying valid, previously unknown, potentially useful patterns in data [3, 4]. However, the volume of data contained in a database often exceeds the ability to analyze it efficiently, resulting in a gap between the collection of data and its understanding [4] A number of successful algorithms for KDD have previously been developed. One particular summarization algorithm, ....

U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Adavances in Knowledge Discovery and Data Mining, pages 1--34. AAAI/MIT Press, 1996.

....analysis of discovered knowledge. Introduction The process of knowledge discovery from databases includes these steps: data selection, cleaning and other preprocessing, reduction and transformation, data mining to identify interesting patterns, interpretation and evaluation, and application [7]. The goal is to identify valid, previously unknown, potentially useful patterns in data [7; 9] The data mining step requires the choice of four items: a data mining task (such as prediction, description, or anomaly detection) a representation language for patterns, evaluation criteria for ....

U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery. In U.M. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, editors, Adavances in Knowledge Discovery and Data Mining, pages 1--34. AAAI/MIT Press, 1996.

....is discovering association rules [1, 2] Association rules are generally used with basket, census or financial data. On the other hand, medical data is generally analyzed with classifier trees, clustering, or regression, but rarely with association rules. A survey on these techniques is found in [10]. In this work we analyze the idea of discovering constrained association rules in medical records that include numeric, categorical, time and image data. This work is based on a long time joint research effort by Georgia Tech and Emory University to discover knowledge in medical data to predict ....

....Closer to 1 indicates absence of a perfusion defect. Each of the artery fields has a value between 0 and 100, and each heart region has a value between 1 and 1. 2.4. Alternative approaches Here we explain why other data mining techniques are inadequate to solve our problem. Decision trees [10] produce rules to classify records from a data set minimizing classification error. This approach assumes there is a target variable indicating the class to which each record belongs. In our case it would have to be a categorical variable indicating if the patient is healthy or sick. However, ....

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Usama Fayyad and G. Piateski-Shapiro. From Data Mining to Knowledge Discovery. MIT Press, 1995.

....probably both for web usage mining and collaborative filtering reasons) 2 DRAFT 2 Background theory 2. 1 Knowledge discovery and Data Mining Knowledge discovery is the non trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data [Fay96a] Data mining is the application of one or several data mining algorithms in a knowledge discovery process with the purpose of extracting patterns from a set of example data and possibly some background knowledge. Applying ILP in automatic synthesis of logic rules can thus be considered as a Data ....

Fayyad U.M., Piatetsky-Shapiro G. and Smyth P. From Data Mining to Knowledge Discovery. In Fayyad U.M., PiatetskyShapiro G., Smyth P. and Uthurusamy R., editor, Advances in Knowledge Discovery and Data Mining, pages 1--34. MIT Press, 1996. 2.1

....most popular approaches to do data mining is discovering association rules [1, 2] Association rules are generally used with basket, census or financial data. Medical data is generally analyzed with classifier trees, clustering, or regression. For an excellent survey on these techniques consult [11]. In this work we explore the idea of discovering association rules in medical data, which we believe to be an untried approach. One of the most important features of association rules is that they are combinatorial in nature. This is particularly useful to discover patterns that appear in ....

Usama Fayyad and G. Piateski-Shapiro. From Data Mining to Knowledge Discovery. MIT Press, 1995.

....of the KDD process and demonstrates future potential. Initially the targeted users for this facility are the operator and application developer community. 2 Knowledge discovery in databases It has been shown that the amount of information stored in databases is dramatically increasing everyday [2]. On that same token the tools and facilities used to understand and analyze this data has not increased at the same rate [2] This is further compounded by the realization that efficient and intelligent analysis of this data can be a valuable asset to any process. To address these issues the ....

....developer community. 2 Knowledge discovery in databases It has been shown that the amount of information stored in databases is dramatically increasing everyday [2] On that same token the tools and facilities used to understand and analyze this data has not increased at the same rate [2]. This is further compounded by the realization that efficient and intelligent analysis of this data can be a valuable asset to any process. To address these issues the computer science community has created a new field called Knowledge discovery in databases. A formal definition of Knowledge ....

Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996. From Data Mining to Knowledge Discovery. In Advances In Knowledge Discovery and Data Mining,ed. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P, and Uthurusamy, R. cambridge, Mass: AAAI/MIT Press, 1-31.

....meaning. Definition 1 (The Data Mining Process) We define the Data Mining Process as being the process of extracting patterns from selected data using low level methods such as logical, statistical or neuronal methods. We distinguish Data Mining from Knowledge Discovery in Databases following (Fayyad, 1994) which involves a complete process of data selection, an iterative an interactive mining process as well as an important phase of interpretation of the mining process. Definition 2 (Textual Data) The textual data is a set of texts to be mined. We consider a text as an unsplitable set of ....

Fayyad U.M., Piatetsky-Shapiro G.F. & Smyth P. (1994). From Data Mining to Knowledge Discovery, Chapitre 1.

....from the literature that have been successfully employed in data mining applications. 1 1 Introduction Knowledge discovery in databases, also known as data mining, is the efficient discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases [19, 15]. Ultimately, the knowledge that we seek to discover describes patterns in the data as opposed to knowledge about the data itself. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, sequential patterns, time series, ....

U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 1--34. AAAI/MIT Press, 1996.

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U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery. AI Magazine, fall:37--54, 1996.

....usefulness in discovering multivariate relationships, and second, because they require a lot of work by the investigator it is tedious and very time consuming to apply them to data sets with large numbers of variables. Exploratory data analysis therefore has much in common with data mining ( 11] [9]) The fundamental objective is clearly the same: discovery of relationships in large bodies of data. This suggests that it should be possible to use data mining techniques to automate EDA, thus solving both of the problems arising from the limitations of conventional EDA techniques. 1.2 SNOUT: ....

U. M. Fayyad, P. Piatetsky-Shapiro, and P. Smyth. From Data Mining to Knowledge Discovery. In Advances in Knowledge Discovery and Data Mining, pages 1--34. The MIT Press, Cambridge, Mass., 1996.

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U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, From Data Mining to Knowledge Discovery, in Advances in Knowledge Discovery and Data Mining, U. Fayyad, et. al. (Eds.), AAAI/MIT press, 1996.

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