Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI

Kosters, Walter A.; Pijls, Wim; Popova, Viara

doi:10.1007/3-540-45065-3_25

Cited by 26 publications

(14 citation statements)

References 3 publications

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“…In Kosters et al (2003), a practical complexity analysis is also performed, showing that FP-Growth performs reasonably well when there are fewer users compared to the number of items, which is true in a model learning setting, as in our case, in which indeed |M| \\ jI k ðMÞj for any k (see Sect. 4).…”

Section: Efficiency Of Itemset Miningmentioning

confidence: 93%

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Recommendation of microblog users based on hierarchical interest profiles

Faralli

Stilo

Velardi

2015

Soc. Netw. Anal. Min.

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Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based, either on the user's network structure, or on some notion of topical similarity with other users, or on both. In this paper, we propose to accomplish the recommendation task in two steps: First, we profile users and classify them as belonging to a target community (depending e.g., on their political affiliation, preferred football team, favorite coffee shop, etc.). Then, we fine-tune recommendations for selected populations. We cast both problems of user classification and recommendation as one of itemset mining, where items are either users' authoritative friends or semantic categories associated to friends, extracted from WiBi, the Wikipedia Bitaxonomy. In addition to evaluating our profiler and recommender on several populations, we also show that semantic categories allow for very finegrained population studies, and make it possible to recommend not only whom to follow, but also topics of interest, users interested in the same topic, and more.

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Section: Efficiency Of Itemset Miningmentioning

confidence: 93%

“…The complexity of FP-Growth is analyzed in Kosters et al (2003). The complexity of creating the FP-tree is order of the dimension of the transactions database, since it requires two passes through the database; therefore, in our case, it is order of |M|.…”

Section: Efficiency Of Itemset Miningmentioning

confidence: 99%

Recommendation of microblog users based on hierarchical interest profiles

Faralli

Stilo

Velardi

2015

Soc. Netw. Anal. Min.

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“…We also vary the slide and window sizes, but the maximum memory usages for these methods are similar to those in Figure 6. This is because the memory cost of all the methods are more influenced by the (iii) cost, while (iii) cost is influenced by both the window size and the number of distinct items [11,3].…”

Section: Efficiency Of Counting Algorithmmentioning

confidence: 99%

Mining frequent itemsets over tuple-evolving data streams

Zhang

Mazuran

Mousavi

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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In many data streaming applications today, tuples inside the streams may get revised over time. This type of data stream brings new issues and challenges to the data mining tasks. We present a theoretical analysis for mining frequent itemsets from sliding windows over such data. We define conditions that determine whether an infrequent itemset will become frequent when some existing tuples inside the streams have been updated. We design simple but effective structures for managing both the evolving tuples and the candidate frequent itemsets. Moreover, we provide a novel verification method that efficiently computes the counts of candidate itemsets. Experiments on real-world datasets show the efficiency and effectiveness of our proposed method.

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“…Regarding to the PatternMining sub-function (lines 16-37), the algorithm basically checks properties of the itemset X to extract spatio-temporal patterns. If X satisfies the mint condition then X is a closed swarm (lines [18][19]. After that, we check the consecutive time constraint for convoy and moving cluster (lines [21][22] and then if the convoy satisfies mint condition and correctness in terms of objects containing (line 31), output convoy (line 32).…”

Section: Get_movementioning

confidence: 99%

GeT_Move: An Efficient and Unifying Spatio-temporal Pattern Mining Algorithm for Moving Objects

Hai

Poncelet

Teisseire

2012

Advances in Intelligent Data Analysis XI

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Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, these object sets are called spatio-temporal patterns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pattern. In addition to being a painstaking task due to the large number of algorithms used to mine and manage patterns, it is also time consuming. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine spatio-temporal patterns in the itemset context. Secondly, we propose a unifying approach, named GeT Move, which uses a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT Move is implemented in two versions which are GeT Move and Incremental GeT Move. To optimize the efficiency and to free the parameters setting, we also propose a Parameter Free Incremental GeT Move algorithm. Comprehensive experiments are performed on real datasets as well as large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches.

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Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI

Cited by 26 publications

References 3 publications

Recommendation of microblog users based on hierarchical interest profiles

Recommendation of microblog users based on hierarchical interest profiles

Mining frequent itemsets over tuple-evolving data streams

GeT_Move: An Efficient and Unifying Spatio-temporal Pattern Mining Algorithm for Moving Objects

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