Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection | Date: 28 April 2011, 06:48
|
Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection (Advances in Data Warehousing and Mining (Adwm) Book)
By Yun Sing Koh, Nathan Rountree
* Publisher: Information Science Reference
* Number Of Pages: 301
* Publication Date: 2009-08-25
* ISBN-10 / ASIN: 1605667544
* ISBN-13 / EAN: 9781605667546
Product Description:
The growing complexity and volume of modern databases make it increasingly important for researchers and practitioners involved with association rule mining to make sense of the information they contain. Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection provides readers with an in-depth compendium of current issues, trends, and technologies in association rule mining. Covering a comprehensive range of topics, this book discusses underlying frameworks, mining techniques, interest metrics, and real-world application domains within the field.
Table of Contents:
Section I: Beyond the Support-Confidence Framework
Chapter I: Rare Association Rule Mining: An Overview
The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule mining systems, such as Apriori, generally employ an exhaustive search algorithm. While these algorithms are in theory capable of finding rare association rules, they become intractable if the minimum level of support is set low enough to find rare rules. Such algorithms are therefore inadequate for finding rare associations, and also suffer from the rare item problem. Research to solve this problem has become more prevalent in recent times. The main goal of rare association rule mining is to discover relationships among sets of items in a transactional database that occur infrequently. This chapter presents a survey on the current trends and approaches in the area of rare association rule mining.
Chapter II: Association Rule and Quantitative Association Rule Mining among Infrequent Items
Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the authors propose two simple, practical and effective schemes to mine association rules among rare items. Our algorithms can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. Their schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, they explore quantitative association rule mining in transactional databases among infrequent items by associating quantities of items: some interesting examples are drawn to illustrate the significance of such mining.
Chapter III: Replacing Support in Association Rule Mining
Association rules are an intuitive descriptive paradigm that has been used extensively in different application domains with the purpose to identify the regularities and correlation in a set of observed objects. However, association rules’ statistical measures (support and confidence) have been criticized because in some cases they have shown to fail in their primary goal: that is to select the most relevant and significant association rules. In this paper the authors propose a new model that replaces the support measure. The new model, like support, is a tool for the identification of reliable rules and is used also to reduce the traversal of the itemsets’ search space. The proposed model adopts new criteria in order to establish the reliability of the information extracted from the database. These criteria are based on Bayes’ Theorem and on an estimate of the probability density function of each itemset. According to our criteria, the information that we have obtained from the database on an itemset is reliable if and only if the confidence interval of the estimated probability is low compared with the most likely value of it. The authors will see how this method can be computed in an approximate but satisfactory way, with the same algorithms that are usually adopted to select itemsets on support threshold.
Chapter IV: Effective Mining of Weighted Fuzzy Association Rules
A novel approach is presented for effectively mining weighted fuzzy association rules (ARs). The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance wrt some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. The authors generalize the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. Their approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The chapter presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach.
Section II: Dealing with Imbalanced Datasets
Chapter V: Rare Class Association Rule Mining with Multiple Imbalanced Attributes
In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.
Chapter VI: A Multi-Methodological Approach to Rare Association Rule Mining
Rare association rule mining has received a great deal of attention in the past few years. In this paper, we propose a multi methodological approach to the problem of rare association rule mining that integrates three different strands of research in this area. Firstly, the authors make use of statistical techniques such as the Fisher test to determine whether itemsets co-occur by chance or not. Secondly, they use clustering as a pre-processing technique to improve the quality of the rare rules generated. Their third strategy is to weigh itemsets to ensure upward closure, thus checking unbounded growth of the rule base. Their results show that clustering isolates heterogeneous segments from each other, thus promoting the discovery of rules which would otherwise remain undiscovered. Likewise, the use of itemset weighting tends to improve rule quality by promoting the generation of rules with rarer itemsets that would otherwise not be possible with a simple weighting scheme that assigns an equal weight to all possible itemsets. The use of clustering enabled us to study in detail an important sub-class of rare rules, which we term absolute rare rules. Absolute rare rules are those are not just rare to the dataset as a whole but are also rare to the cluster from which they are derived.
Chapter VII: Find
|
DISCLAIMER:
This site does not store Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection on its server. We only index and link to Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection provided by other sites. Please contact the content providers to delete Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection if any and email us, we'll remove relevant links or contents immediately.
|
 |
|
Comments (0)
All
