FreeSpan

Han, Jiawei; Pei, Jian; Mortazavi-Asl, Behzad; Chen, Qiming; Dayal, Umeshwar; Hsu, Meichun

doi:10.1145/347090.347167

Cited by 597 publications

(36 citation statements)

References 4 publications

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“…This algorithm is based on the pattern-growth strategy used in [38]. The principle of this approach is to extract frequent patterns without a candidate generation step.…”

Section: Mining Sequential Patternsmentioning

confidence: 99%

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

Alatrista-Salas

Azé

Bringay

et al. 2015

Ecological Informatics

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Rapid population growth, and human activities (such as agriculture, industry, transports,...) development have increased vulnerability risk for water resources. Due to the complexity of natural processes and the numerous interactions between hydro-systems and human pressures, water quality is difficult to be quantified. In this context, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre-processed in order to obtain different spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. Finally we propose a combination of two techniques (1) to filter patterns based on interest measure, and; (2) to group and present them graphically, to help the experts. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data.

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“…This algorithm is based on the pattern-growth strategy used in [38]. The principle of this approach is to extract frequent patterns without a candidate generation step.…”

Section: Mining Sequential Patternsmentioning

confidence: 99%

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

Alatrista-Salas

Azé

Bringay

et al. 2015

Ecological Informatics

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“…A representative list of the well-known sequential mining algorithms include Apriori-based GSP (Generalized Sequential Patterns), [25] Pattern-growth based FreeSpan [26] and PrefixSpan, [27] Vertical format-based SPADE [28] and Constraint-based SPIRIT. [29] All of these solutions mainly focus on the sub-sequence mining, which is to find the common part of all the sequences in a dataset.…”

Section: Related Workmentioning

confidence: 99%

Heavy path based super-sequence frequent pattern mining on web log dataset

Korkmaz

2015

AIR

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Mining web log datasets has been extensively studied using Frequent Pattern Mining (FPM) and its various other forms. Identifying frequent patterns in different sequences can help in analyzing the most common sub-sequences (e.g., the pages visited together). However, this approach would not be able to identify general structures spanning over multiple sequences. In response to understanding general structures, we introduce a new form of sequential pattern mining called super-sequence frequent pattern mining (SS-FPM). In contrast to sub-sequences determined by FPM, SS-FPM determines the super-sequences that can contain the common parts from different sequences. This can be useful in understanding the general behavior/flow of users in web usage mining, classifying web pages and users, making predictions etc. In essence, finding frequent super-sequence patterns turns out to be related to the well-known heaviest (longest) path problem in graphs, which is known to be NP-hard. Accordingly, we transform a given sequential dataset into a sequence graph and formulate the problem as k-hop heaviest path problem. We then propose an efficient heuristic called sequence matrix method using dynamic programming techniques. We compared our method to the existing Heavypath method. The results show that our method is more efficient especially on large datasets.

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“…Recently, the FreeSpan (Frequent pattern-projected Sequential Pattern Mining) algorithm was proposed to mine sequential patterns by a database projection technique [4]. Based on a similar projection technique, the authors in [12] proposed the PrefixSpan (Prefix-projected Sequential pattern mining) algorithm.…”

Section: Algorithms For Sequential Pattern Miningmentioning

confidence: 99%

“…Mining sequential patterns, which finds out temporal associations among item-sets in the sequence database, is an important issue in data mining [2,4,6,7,13,16]. A classic application of the problem is the market basket analysis whose database contains purchase records, where each record is an ordered sequence of itemsets (sets of items) bought by a customer.…”

Section: Introductionmentioning

confidence: 99%

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Improving the efficiency of interactive sequential pattern mining by incremental pattern discovery

Lin

Lee

2003

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of The

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The discovery of sequential patterns, which extends beyond frequent item-set finding of association rule mining, has become a challenging task due to its complexity. Essentially, a user would specify a minimum support threshold with respect to the database to find out the desired patterns. The mining process is usually iterative since the user must try various thresholds to obtain the satisfactory result. Therefore, the time-consuming process has to be repeated several times. However, current approaches are inadequate for such process due to the long execution time required for each trial. In order to minimize the total execution time and the response time for each trial, we propose a knowledge base assisted algorithm for interactive sequence discovery, called KISP. KISP constructs a knowledge base accumulating the pattern information in individual mining, eliminates considerable amount of potential patterns to facilitate efficient support counting, and speeds up the whole process. In addition, we further optimize the algorithm by direct generations of the reduced candidate sets and concurrent counting of variable sized candidates. For some queries, KISP may eliminate database access completely. The conducted experiments show that KISP outperforms GSP, a state-of-the-art sequence mining algorithm, by several orders of magnitudes for interactive sequence discovery.

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FreeSpan

Cited by 597 publications

References 4 publications

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring

Heavy path based super-sequence frequent pattern mining on web log dataset

Improving the efficiency of interactive sequential pattern mining by incremental pattern discovery

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