R.J. Brahmann, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro, E. Simondis, "Mining Business Databases", Communications of the ACM, 39(11), pp. 42-48, 1996. Home/Search Document Details and Download
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DIRECT: a system for mining data value conversion rules.. - Fan, Lu, Madnick, Cheung (Correct)
.... For example, data mining has been used for customer profiling in CRM and customer service support [17] credit card application approval, fraud detection, telecommunication network monitoring, market basket analysis [11] healthcare quality assurance [36] and many other decision making areas [3]. In this paper, we are extending the data mining paradigm to a new application area: data integration from disparate sources [10,21] in which database administrators often find it very hard to make decisions on the integration of those semantically related attributes [28] It is known that the ....
R. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro, E. Simoudis, Mining business databases, Communications of ACM 39 (11) (1996) 42 -- 48.
An Application of Inductive Learning for Mining Basket Data - Uysal, Güvenir (1999) (Correct)
....today s databases are still waiting to be processed for knowledge, instead of being left as large archives. A description of the field and the process of knowledge discovering in databases are given in [7] and some applications of data mining on scientific and business databases are described in [3, 8]. The development of bar code technology, and its utilization in markets has opened a new application field for data miners. The market databases, so called basket data, filled by the transactions of this technology is large enough to obtain significant results and accurate enough since the ....
R.J. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky - Shapiro, E. Simoudis, Mining Business Databases, Communications of the ACM, November 1996.
A Data Mining Framework for Constructing Features and Models for.. - Lee (1999) (17 citations) (Correct)
....patterns can be computed e# ciently; automatically constructing features from the mined patterns; and e#cient real time execution of detection models. 3. 4 Related Data Mining Applications Data mining (KDD) techniques have been successfully applied to many business and scientific domains [ Brachman et al. 1996; Fayyad et al. 1996a ] including some that are closely related to intrusion detection. Cellular fraud detection is similar to intrusion detection in that unusual behavior is to be distinguished from typical behavior. Historically, manual and ad hoc approaches were used to decide which aspects ....
R. J. Brachman, T. Khabaza, W. Kloesgen, G. PiatetskyShapiro, and E. Simoudis. Mining business databases. Communications of the ACM, 39(11):42--48, November 1996.
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....or to make more money by discovering ways to sell more products to customers. For instance, companies are using data mining to discover which products sell well at which times of year, so they can manage their retail store inventory more eciently, potentially saving millions of dollars a year [6]. Other companies are using KDD to discover which customers will be most interested in a special o er, reducing the costs of direct mail or outbound telephone campaigns by hundreds of thousands of dollars a year [3, 18] These applications typically involve using data mining to discover a new ....
Brachman, R., J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. Mining Business Databases. Communications of the ACM, 39(11), pp. 42-48, November.
Analysis of Recommendation Algorithms for E-Commerce - Sarwar, Karypis, Konstan.. (2000) (18 citations) (Correct)
....or to make more money by discovering ways to sell more products to customers. For instance, companies are using data mining to discover which products sell well at which times of year, so they can manage their retail store inventory more eciently, potentially saving millions of dollars a year [6]. Other companies are using KDD to discover which customers will be most interested in a special o er, reducing the costs of direct mail or outbound telephone campaigns byhundreds of thousands of dollars a year [3, 18] These applications typically involve using data mining to discover a new ....
Brachman, R., J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. Mining Business Databases. Communications of the ACM, 39(11), pp. 42-48, November.
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....large and inherently distributed databases of information about transaction behaviors to produce models of probably fraudulent transactions. It is an emerging technique, people in AT T, NYNEX and GTE are using this method to develope cellular phone fraud detecting system on centralized databases [18]. The key difficulties in this approach are: financial companies don t share their data for a number of (competitive and legal) reasons; the databases that companies maintain on transaction behavior are huge and growing rapidly; real time analysis is highly desirable to update models when new ....
R. Branchman, T. Khabaza, W. Kloesgen and et all. Mining Business Databases. In CACM, vol.11, November 1997, pages 41--48
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....models for acquiring knowledge from facts and background knowledge. These and related efforts have led to the emergence of a new research area, frequently called data mining and knowledge discovery, e.g. Lbo81] MBS82] ZG89] Mic91b] Zag91] MKKR92] VHMT93] FPSU96] EH96] [BKKPS96], and [FHS96] The first part of this chapter is a compendium of ideas on the applicability of symbolic machine learning methods to data mining and knowledge discovery. While this chapter concentrates on methods for extracting knowledge from numeric and symbolic data, many DATA MINING AND ....
Brachman, R.J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G. and Simoudis, E. Mining Business Databases. Communications of the ACM, 39:11, pp. 42-48, 1996.
Neural Networks Based Forecasting Techniques for Inventory Control .. - Kanti (1998) Self-citation (Piatetsky-shapiro) (Correct)
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Brachman, R.J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. Mining business databases. Communications of the ACM, 39:11.
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R.J. Brahmann, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro, E. Simondis, "Mining Business Databases", Communications of the ACM, 39(11), pp. 42-48, 1996.
Item-based Collaborative Filtering Recommendation.. - Sarwar, Karypis, Konstan, .. (2001) (39 citations) (Correct)
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Brachman, R., J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. Mining Business Databases. Communications of the ACM, 39(11), pp. 42-48, November.
Item-based Collaborative Filtering Recommendation.. - Sarwar, Karypis, Konstan, .. (2001) (39 citations) (Correct)
No context found.
Brachman, R., J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. Mining Business Databases. Communications of the ACM, 39(11), pp. 42-48, November.
Item-based Collaborative Filtering Recommendation.. - Sarwar, Karypis, Konstan, .. (2001) (39 citations) (Correct)
No context found.
Brachman, R., J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. Mining Business Databases. Communications of the ACM, 39(11), pp. 42-48, November. 14
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Brachman, R., J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., and Simoudis, E. 1996. "Mining Business Databases." Communications of the ACM, 39(11), pp. 42-48, November.
Stock Miner: A System For Knowledge Discovery In.. - Karamanlidou, Tuffier.. (Correct)
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R.J. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro and E. Simoudis. Mining business databases. Communications of the ACM, 39(11):42-48, 1996.
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