Mining maximal frequent intervals

Lin, Jun-Lin

doi:10.1145/952613.952617

Cited by 10 publications

(30 citation statements)

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“…In [1] the notion of frequent and maximal frequent intervals for a given database of intervals is defined and an algorithm is presented for the determination of the maximal frequent interval. The algorithm consists of two stages.…”

Section: Related Workmentioning

confidence: 99%

“…This was a considerable improvement since in general log is far less than | | + | |. In the tests with experimental data with synthetic dataset as mentioned in [1], the construction of ITree has taken the most amount of time in the whole algorithm. Therefore the method in [2] leads to a considerable improvement in the amount of time taken in the execution of the entire algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper as shown in the following sections, we have given a method of obtaining the minimal infrequent intervals for a given database of intervals, after obtaining the maximal frequent intervals. The work in [1] and [2] are for databases of intervals in single dimension only. As mentioned in [1] extension to multi-dimensional intervals will be done in future.…”

Section: Related Workmentioning

confidence: 99%

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Mining Minimal Infrequent Intervals

Dutta¹

2018

IJCA

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Many real world data are closely associated with the interval of time and distance. Mining infrequent intervals from such data allows users to group transactions with less similarity while mining frequent interval allows user to group the transaction with a similarity above a certain measure. In [1], the notion of mining maximal frequent interval in either a discrete domain or continuous domain is introduced. This paper presents an effective minimal infrequent interval finding algorithm (MII) based on two maximal frequent interval finding techniques represented in [1] and [2] the proposed MII has been established to be effective both theoretically and experimentally.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mining Minimal Infrequent Intervals

Dutta¹

2018

IJCA

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“…Several approaches [17,30] consider discovering frequent intervals in databases, where intervals appear sequentially and are not labelled, while others [7] consider temporally annotated sequential patterns where transitions from one event to another have a time duration. A graph-based approach [10] represents each temporal pattern by a graph considering only two types of relations between events (follow and overlap).…”

Section: Related Workmentioning

confidence: 99%

ARTEMIS: Assessing the Similarity of Event-Interval Sequences

Kostakis

Papapetrou

Hollmén

2011

Machine Learning and Knowledge Discovery in Databases

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Abstract. In several application domains, such as sign language, medicine, and sensor networks, events are not necessarily instantaneous but they can have a time duration. Sequences of interval-based events may contain useful domain knowledge; thus, searching, indexing, and mining such sequences is crucial. We introduce two distance measures for comparing sequences of interval-based events which can be used for several data mining tasks such as classification and clustering. The first measure maps each sequence of interval-based events to a set of vectors that hold information about all concurrent events. These sets are then compared using an existing dynamic programming method. The second method, called Artemis, finds correspondence between intervals by mapping the two sequences into a bipartite graph. Similarity is inferred by employing the Hungarian algorithm. In addition, we present a linear-time lowerbound for Artemis. The performance of both measures is tested on data from three domains: sign language, medicine, and sensor networks. Experiments show the superiority of Artemis in terms of robustness to high levels of artificially introduced noise.

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“…There have been several approaches on discovering intervals that occur frequently in a transactional database (Lin, 2003;Lin, 2002). In most cases, however, the intervals are unlabelled and no relations between them are considered.…”

Section: Temporal Mining and Association Rulesmentioning

confidence: 99%

Mining frequent arrangements of temporal intervals

et al. 2009

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Abstract. The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.

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Mining maximal frequent intervals

Cited by 10 publications

References 0 publications

Mining Minimal Infrequent Intervals

Mining Minimal Infrequent Intervals

ARTEMIS: Assessing the Similarity of Event-Interval Sequences

Mining frequent arrangements of temporal intervals

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