Efficient and Accurate Discovery of Colossal Pattern Sequences from Biological Datasets: A Doubleton Pattern Mining Strategy (DPMine)

Prasanna, K.; Seetha, M.

doi:10.1016/j.procs.2015.06.048

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…An algorithm termed Pattern-Fusion [8] was developed to mine colossal itemset by skipping small cardinality itemsets which are less useful for scientists. Since then, several colossal (closed) itemset mining algorithm were developed in the effort to discover useful and interesting knowledge from high-dimensional data, including CPM [9], DPMine [10], BVBUC [11], DisClose [12] and CP-Miner [13]. CPM, DPMine, DisClose and CP-Miner store the discovered colossal closed itemsets in a prefix tree, while BVBUC uses a text file which resulted to longer running time to check for duplicates.…”

Section: Closed Frequent Itemset Mining Methodsmentioning

confidence: 99%

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Zaki

Zulkurnain

2019

AMM

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The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have been proposed to achieve these goals. However, to the best of our knowledge, there is no suitable method that can be adapted for algorithms that enumerate the rowset lattice, which is effective for biological datasets. Therefore, this paper proposed a method for computing closure compare with the method used in BVBUC algorithm method. Finally, BVBUC_I is proposed and the performances of these algorithms were evaluated using two synthetic datasets and three real datasets. The results of these tests proved the efficiency of the proposed method.

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Section: Closed Frequent Itemset Mining Methodsmentioning

confidence: 99%

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Zaki

Zulkurnain

2019

AMM

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“…The computation of support is based on dynamic bit vectors when generating new patterns. These bit vectors can also be used in mining web access patterns [31]. Trang et al proposed two algorithms named MWAPC and EMWAPC, which are based on the prefix-web access pattern tree (PreWAP) structure for mining web access patterns with a super-pattern constraint.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Bit-Based Approach for Mining Skyline Periodic Itemset Patterns

Li,

2023

Electronics

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Periodic itemset patterns (PIPs) are widely used in predicting the occurrence of periodic events. However, extensive redundancy arises due to a large number of patterns. Mining skyline periodic itemset patterns (SPIPs) can reduce the number of PIPs and guarantee the accuracy of prediction. The existing SPIP mining algorithm uses FP-Growth to generate frequent patterns (FPs), and then identify SPIPs from FPs. Such separate steps lead to a massive time consumption, so we propose an efficient bit-based approach named BitSPIM to mine SPIPs. The proposed method introduces efficient bitwise representations and makes full use of the data obtained in the previous steps to accelerate the identification of SPIPs. A novel cutting mechanism is applied to eliminate unnecessary steps. A series of comparative experiments were conducted on various datasets with different attributes to verify the efficiency of BitSPIM. The experiment results demonstrate that our algorithm significantly outperforms the latest SPIP mining approach.

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“…Repetition detection in biological data is required for potential malfunction and disease identification. DPMine [23] is designed for colossal sequence discovery from biological dataset. It integrates a DPT+ tree, a doubleton data matrix and a one-dimensional array to find doubleton patterns which may further generate colossal sequences.…”

Section: Introductionmentioning

confidence: 99%

Mining frequent biological sequences based on bitmap without candidate sequence generation

Wang

Davis

Ren

2016

Computers in Biology and Medicine

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Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability.

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Efficient and Accurate Discovery of Colossal Pattern Sequences from Biological Datasets: A Doubleton Pattern Mining Strategy (DPMine)

Cited by 6 publications

References 8 publications

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

Improved BVBUC Algorithm to Discover Closed Itemsets in Long Biological Datasets

An Efficient Bit-Based Approach for Mining Skyline Periodic Itemset Patterns

Mining frequent biological sequences based on bitmap without candidate sequence generation

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