The Association Rule Algorithm with Missing Data in Data Mining

Gerardo, Bobby D.; Lee, Jaewan; Lee, Jung-Sik; Park, Mingi; Lee, Malrey

doi:10.1007/978-3-540-24707-4_13

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…This algorithm is the extension of the discover maximum frequent itemsets algorithm (DMFiA) proposed in Song et al (2003);gerardo et al (2004) to discover the maximum frequent itemsets. in this work, we call it the multiple minimum supports for the discovery of maximum frequent itemsets algorithm (MSDMFiA).…”

Section: Association Rule Mining Algorithm With Multiple Supportsmentioning

confidence: 99%

Algorithm for mining association rules with multiple minimum supports based on FP‐Tree

Zhao

Huarui²,

Sun

et al. 2007

New Zealand Journal of Agricultural Research

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Association rule mining algorithms can be used to discover all item associations (or rules) in a dataset that satisfy user-specified constraints, e.g., minimum supports (minsup) and minimum confidence (minconf). Since only one minsup is used for the whole database, it is implicitly assumed in the model that all items are of the same nature and/ or have similar frequencies in the data. This rarely applies in reality, so, based on an FP-Tree, a new algorithm is proposed called multiple minimum supports for discovering maximum frequent item sets algorithm (MSDMFIA). The algorithm allows users to specify multiple minsups to reflect item natures and various frequencies, which resolves bottlenecks in traditional algorithms, e.g., the frequent generation of candidate itemsets and database scanning. Experimental results show that functionality and performance of the proposed algorithm is significantly improved compared with most others.

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Section: Association Rule Mining Algorithm With Multiple Supportsmentioning

confidence: 99%

Algorithm for mining association rules with multiple minimum supports based on FP‐Tree

Zhao

Huarui²,

Sun

et al. 2007

New Zealand Journal of Agricultural Research

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“…Missing data can be handled by various ways: ignoring the records, filling the missing values manually, or using a global constant, or attribute mean or most probable value by inference based on Bayesian formula or decision trees. Missing values are observed when certain stocks or indices record null values on certain days [4,12,25].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Due to the large size of databases, importance of information stored, and valuable information obtained, it has become increasingly significant to find hidden patterns in data has become increasingly significant [16,21]. The stock market provides an area in which large volumes of data is created and stored on a daily basis, and hence an ideal dataset for applying data mining techniques [1,4,8,15]. Every investor wants to know or predict the trends of the stock trading.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing and Symbolic Representation of Stock Data

Kumar

Kalia

2012

2012 Second International Conference on Advanced Computing &Amp; Communication Technologies

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“…• Gerardo et al employed the Apriori algorithm to handle the missing data (7). They use pairwise deletion and casewise deletion for deletion of wrong data, and mean substitution for recovering and deduction of missing value.…”

Section: Related Workmentioning

confidence: 99%

Efficient Data Mining Method to Localise Errors in RFID Data

Stantić¹,

Chang²

2010

Artificial Intelligence and Applications

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Since the emergence of Radio Frequency Identification technology (RFID), the community has been promised a cost effective and efficient means to identify and track large number of items with relative ease. Unfortunately, due to the unreliable nature of the passive architecture, the RFID revolution has been reduced to a fraction of intended audience due to the anomalies. These anomalies are duplicate, positive and negative readings. While duplicate readings and wrong data (false positive) can be easily identified and rectified, that is not the case for false negative or missed readings. To identify missed readings data mining methods can be used. However, due to its vast volume and complex spatio-temporal structure of RFID data, traditional data mining methods are not necessarily directly applicable. In this paper we propose method to identify possible missed RFID readings by applying association rules data mining method. In empirical study we show that our algorithm is accurate and efficient and also we show that it scales well with increased number of rows therefore it is applicable on vast volume on spatio-temporal RFID data.

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The Association Rule Algorithm with Missing Data in Data Mining

Cited by 13 publications

References 2 publications

Algorithm for mining association rules with multiple minimum supports based on FP‐Tree

Algorithm for mining association rules with multiple minimum supports based on FP‐Tree

Preprocessing and Symbolic Representation of Stock Data

Efficient Data Mining Method to Localise Errors in RFID Data

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