Mining positive and negative weighted association rules in medical records without user-specified weights based on HITS model

Xie, Weixin; Wu, Jing

doi:10.1109/bmei.2010.5639578

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The idea was that, all item sets cannot be expected to behave similarly and hence it would be more appropriate to evaluate the behavior of different item sets using different minimum support values. In [7][8][9][10][11][12][13][14][15][16][17][18], methods were published for eliminating the less interesting rules which is one of the most challenging task in mining negative association rules. In [19,20], methods were published to mine negative association rule by minimizing the number of scans of the dataset.…”

Section: Related Workmentioning

confidence: 99%

EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique

Ravi

Khare

2014

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]

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Association Rule Mining is a Data Mining technique which extracts association rules from the given dataset. A good number of research work has been reported in Association Rule Mining algorithms which discovers positive association rules. Amongst them, only a few algorithms have focused on Association Rule Mining algorithms which discovers negative association rules too. Amongst the negative Association Rule Mining algorithms, most of them scans the dataset more than once to identify the frequent item sets and also doesn't guarantee that all the extracted rules are interesting. In order to overcome the above said challenges, EO-ARM, an Efficient and Optimized Positive-Negative Association Rule Mining algorithm has been proposed in this paper. EO-ARM produces both positive as well as negative association rules. It scans the dataset only once (irrespective of the size of dataset) to identify frequent item sets using a two dimensional matrix thereby increasing the efficiency. The two dimensional matrix is conceptually similar to k-map. It also optimizes the association rules by introducing a contingency matrix based correlation measure which prunes less interesting rules thereby overcoming the existing limitations. EO-ARM has been implemented using a binary transaction dataset. Several experiments were performed and an optimal support and confidence threshold has been identified for the given dataset. These optimized support and confidence threshold have been used to find the frequent item sets and generating rules from the dataset. Experimental results also proved that EO-ARM is more efficient in terms of execution time than the standard Apriori algorithm and more optimized in terms of no. of rules generated with the pruning done with the projected correlation measure.

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Section: Related Workmentioning

confidence: 99%

EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique

Ravi

Khare

2014

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]

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“…One of the challenges in the weighted association rule mining is the weight assignment. In [57], authors proposed a self-assigned weighting technique as opposed to user-specified weighting to extract positive and negative association rules. Typical or positive association rules consider only items that appear in the dataset frequently but negative association rules consider negated or absent items, negative association rules are valuable since they can identify items that conflict [4].…”

Section: Association Rule Mining In Clinical Retrievalmentioning

confidence: 99%

Exploiting semantics for improving clinical information retrieval

Babashzadeh

Huang

Daoud

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and going beyond keywords matching.To address these issues, in this study we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. In this study we propose query context modeling to improve the effectiveness of clinical IR systems. To model query contexts we propose two novel approaches to modeling medical query contexts. The first approach concerns modeling medical query contexts based on mining semantic-based AR for improving clinical text retrieval. The query context is derived from the rules that cover the query and then weighted according to their semantic relatedness to the query concepts. In our second approach we model a representative query context by developing query domain ontology.To develop query domain ontology we extract all the concepts that have semantic relationship with the query concept(s) in UMLS ontologies. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to the query concept(s). The query context is then exploited in the patient ii records query expansion and re-ranking for improving clinical retrieval performance. We evaluate this approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model.

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Mining positive and negative weighted association rules in medical records without user-specified weights based on HITS model

Cited by 2 publications

References 12 publications

EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique

EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique

Exploiting semantics for improving clinical information retrieval

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