A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns

Winarko, Edi; Roddick, John F.

doi:10.1109/tkde.2008.20

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Trial comes about uncover it has more opportunity for list building. In [14] Winarko et al (2008) examine the reasonableness of various systems which can be connected for post-handling of worldly examples. As a post-preparing strategy, this technique is to store the created designs in a database and later question the database for chose designs.…”

Section: Related Workmentioning

confidence: 99%

Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

Senthil¹,

Suseendran²

2018

IJET

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Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.

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Section: Related Workmentioning

confidence: 99%

Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

Senthil¹,

Suseendran²

2018

IJET

View full text Add to dashboard Cite

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“…Morzy et al [16] use a group bitmap index structure for retrieving association rules, which is essentially a sequential signature file. Winarko and Roddick [23] use signature files to index temporal patterns. Tuzhilin et al [20] use B+ trees to index the support and the confidence of the rules and use inverted files to index patterns.…”

Section: Related Workmentioning

confidence: 99%

A performance study of three disk-based structures for indexing and querying frequent itemsets

2013

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Frequent itemset mining is an important problem in the data mining area. Extensive efforts have been devoted to developing efficient algorithms for mining frequent itemsets. However, not much attention is paid on managing the large collection of frequent itemsets produced by these algorithms for subsequent analysis and for user exploration. In this paper, we study three structures for indexing and querying frequent itemsets: inverted files, signature files and CFPtree. The first two structures have been widely used for indexing general set-valued data. We make some modifications to make them more suitable for indexing frequent itemsets. The CFP-tree structure is specially designed for storing frequent itemsets. We add a pruning technique based on length-2 frequent itemsets to make it more efficient for processing superset queries. We study the performance of the three structures in supporting five types of containment queries: exact match, subset/superset search and immediate subset/superset search. Our results show that no structure can outperform other structures for all the five types of queries on all the datasets. CFP-tree shows better overall performance than the other two structures.

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Research of Temporal Information Index Strategy Based on HBase

Feng

2017

Procedia Computer Science

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A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns

Cited by 5 publications

References 33 publications

Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

A performance study of three disk-based structures for indexing and querying frequent itemsets

Research of Temporal Information Index Strategy Based on HBase

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