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Probabilistic Measures for Interestingness of Deviations - A Survey

Adnan Masood, Sofiane Ouaguenouni
2013 International Journal of Artificial Intelligence & Applications  
With such data profusion, the core knowledge discovery problem becomes efficient data retrieval rather than simply finding heaps of information.  ...  Since then, this field has progressed and new data mining techniques have been introduced to address the subjective, objective, and semantic interestingness measures.  ...  In first survey in 1999 by [7] on "Knowledge Discovery and Interestingness Measures," the researchers examined an enumeration of 17 measures of rule interestingness, offering a brief description of each  ... 
doi:10.5121/ijaia.2013.4201 fatcat:3bwuzx3egbc7xk62g3zosizrcu

A survey of interestingness measures for knowledge discovery

KEN MCGARRY
2005 Knowledge engineering review (Print)  
These so called interestingness measures are generally divided into two categories: objective measures based on the statistical strengths or properties of the discovered patterns and subjective measures  ...  We evaluate the strengths and weaknesses of the various interestingness measures with respect to the level of user integration within the discovery process.  ...  Figure 1 1 Techniques for Knowledge Discovery. Figure 4 4 Taxonomy of interestingness measuresal., 1998).  ... 
doi:10.1017/s0269888905000408 fatcat:7aiqi4oacvd4hd2sdt2cdqh3na

Integrating and Updating Domain Knowledge with Data Mining

Carsten Pohle
2003 Very Large Data Bases Conference  
We propose to employ formalized domain knowledge for assessing the interestingness of mining results.  ...  Dividing interesting mining results from uninteresting ones still is a laborious task mainly performed by human users.  ...  While the approaches relying on objective interestingness measures try to "guess" the subjective surprisingness from the information about a domain contained in the data itself ( [8] ), subjective measures  ... 
dblp:conf/vldb/Pohle03 fatcat:vmd2nijaw5hnjio5fwzxkwp7om

Knowledge actionability: satisfying technical and business interestingness

Longbing Cao, Dan Luo, Chengqi Zhang
2007 International Journal of Business Intelligence and Data Mining  
In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations.  ...  Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance  ...  Acknowledgements This work is sponsored by Australian Research Council Discovery Grant (DP0773412, LP0775041, DP0667060), and UTS internal grants.  ... 
doi:10.1504/ijbidm.2007.016385 fatcat:kaqnldbu5be5tii3uwjjpk5aqm

What makes patterns interesting in knowledge discovery systems

A. Silberschatz, A. Tuzhilin
1996 IEEE Transactions on Knowledge and Data Engineering  
One of the central problems in the eld of knowledge discovery is the development o f g o o d measures of interestingness of discovered patterns.  ...  Such measures of interestingness are divided into objective measures { those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures  ...  These subjective measures do not depend only on the structure of a rule and on the data used in the discovery process, but also on the user who examines the pattern.  ... 
doi:10.1109/69.553165 fatcat:niobj74avvfrbgpnxktu4c5uju

THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING

LONGBING CAO, CHENGQI ZHANG
2007 International journal of pattern recognition and artificial intelligence  
Driven by this methodology, domain intelligence is not necessary in targeting the demonstration of an algorithm.  ...  The results show that domain-driven data mining has a potential for further enhancing the actionability of mined patterns in real-world situation.  ...  Data used in this paper is from Australian Centrelink and Capital Market CRC, AC3 and SIRCA.  ... 
doi:10.1142/s0218001407005612 fatcat:6ln5hcaxefaftp44kjdywbpjy4

Classification of objective interestingness measures

Lan Phuong Phan, Nghia Quoc Phan, Vinh Cong Phan, Hung Huu Huynh, Hiep Xuan Huynh, Fabrice Guillet
2016 EAI Endorsed Transactions on Context-aware Systems and Applications  
The creation of the interestingness measures for evaluating the quality of the association rule -based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases  ...  In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a  ...  Acknowledgements This research is funded by UK -ASEAN Research Hub Project, VNUK Institute for Research and Executive Education, the University of Danang, Grant number 07/HĐ-UARH).  ... 
doi:10.4108/eai.12-9-2016.151678 fatcat:fwf2c4embfeutixmy2nywfgvxy

Domain-Driven Data Mining

Longbing Cao, Chengqi Zhang
2006 International Journal of Data Warehousing and Mining  
Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user  ...  The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraintbased context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability  ...  ACKNOWLEDGMENTS This work was supported in part by the Australian Research Council (ARC) Discovery Projects (DP0449535 and DP0667060), UTS Chancellor and ECRG funds, National Science Foundation of China  ... 
doi:10.4018/jdwm.2006100103 fatcat:wk465g5n5zesrn2srxeoslb7py

Interestingness a Unifying Paradigm Bipolar Function Composition [article]

Iaakov Exman
2014 arXiv   pre-print
Interestingness is an important criterion by which we judge knowledge discovery. But, interestingness has escaped all attempts to capture its intuitive meaning into a concise and comprehensive form.  ...  The paradigm generality is demonstrated by case studies of new interestingness functions, examples of known functions that fit the framework, and counter-examples for which the paradigm points out to the  ...  A more recent survey of interestingness measures for knowledge discovery is found in (McGarry, 2005 [6] ), from which one can infer that heterogeneity still characterizes the discipline.  ... 
arXiv:1404.0091v1 fatcat:fyjbhj223vdyliyz2jyhlldeo4

Actionable Rules: Issues And New Directions

Harleen Kaur
2007 Zenodo  
Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases.  ...  Measures of interestingness are divided into objective and subjective measures.  ...  Vasudha Bhatnagar, Deptt. of Computer Science, Delhi University, New Delhi, India for their encouragement and support. In addition, great thank to Mr.  ... 
doi:10.5281/zenodo.1330314 fatcat:j2hudpp5pzbjpfuv67v5rfvgoi

Interactive Data Exploration Using Pattern Mining [chapter]

Matthijs van Leeuwen
2014 Lecture Notes in Computer Science  
One of the key challenges is to develop principled methods that allow user-and task-specific information to be taken into account, by directly involving the user in the discovery process.  ...  We live in the era of data and need tools to discover valuable information in large amounts of data. The goal of exploratory data mining is to provide as much insight in given data as possible.  ...  The author is supported by a Postdoctoral Fellowship of the Research Foundation Flanders (FWO). He would like to thank Vladimir Dzyuba for providing useful comments on an early version of this paper.  ... 
doi:10.1007/978-3-662-43968-5_9 fatcat:v3fifyrph5cercw5wip4wppv6m

Development Of Subjective Measures Of Interestingness: From Unexpectedness To Shocking

Eiad Yafi, M. A. Alam, Ranjit Biswas
2007 Zenodo  
In this report, we try to brief the more widely spread and successful subjective measures and propose a new subjective measure of interestingness, i.e. shocking.  ...  Knowledge Discovery of Databases (KDD) is the process of extracting previously unknown but useful and significant information from large massive volume of databases.  ...  In this survey, we will list all the subjective measures of interestingness which have been introduced before and we Eiad Yafi is a research scholar from Syria, working on his PhD in Data Mining at Hamdard  ... 
doi:10.5281/zenodo.1330415 fatcat:rfcbflzqcnagrgnzd3suboj7lu

Flexible Frameworks for Actionable Knowledge Discovery

Longbing Cao, Yanchang Zhao, Huaifeng Zhang, Dan Luo, Chengqi Zhang, E K Park
2010 IEEE Transactions on Knowledge and Data Engineering  
In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives.  ...  Index Terms-Data mining, domain-driven data mining (D 3 M), actionable knowledge discovery, decision making.  ...  ACKNOWLEDGMENTS This work is sponsored in part by the Australian Research Council Discovery Grants (DP0988016, DP0773412, and DP0667060) and ARC Linkage Grant (LP0989721 and LP0775041).  ... 
doi:10.1109/tkde.2009.143 fatcat:4f4l5lkf5ngnxlhj5zsj5bzlmq

Finding interesting patterns using user expectations

Bing Liu, Wynne Hsu, Lai-Fun Mun, Hing-Yan Lee
1999 IEEE Transactions on Knowledge and Data Engineering  
AbstractÐOne of the major problems in the field of knowledge discovery (or data mining) is the interestingness problem.  ...  Past research and applications have found that, in practice, it is all too easy to discover a huge number of patterns in a database.  ...  ACKNOWLEDGMENTS We would like to thank Hwee-Leng Ong, Angeline Pang, King-Hee Ho, Poh-San Lai, Kok-Seng Pun, Kheng-Cheong See, Gui-Jun Yang, Shu Chen, and six of our undergraduate project students for  ... 
doi:10.1109/69.824588 fatcat:jeseklqnobebpgqeyqdsk6fqce

D 3 M Methodology [chapter]

Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
2009 Domain Driven Data Mining  
On the basis of the discussions and retrospection on existing data mining methodologies and techniques in Chapter 1, this chapter presents an overall picture of domain driven data mining (D 3 M).  ...  We focus on the high level of architecture and concepts of the D 3 M methodology.  ...  and subjective interestingness.  ... 
doi:10.1007/978-1-4419-5737-5_2 fatcat:wu74nh4umnejnm7ax3igwsmxqy
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