Mining constrained frequent itemsets from distributed uncertain data

Cuzzocrea, Alfredo; Leung, Carson K.; MacKinnon, Richard Kyle

doi:10.1016/j.future.2013.10.026

Cited by 72 publications

(10 citation statements)

References 34 publications

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“…As a preview, we will reduce a social network analysis problem of finding the interesting ‘following’ patterns into a data mining problem. Several data mining algorithms and techniques have been proposed over the past few years for mining social networks such as the discovery of special events , detection of communities , subgraph mining , as well as discovery of popular friends , influential friends and strong friends . Given that we will reduce our social network analysis problem into a specific data mining problem of frequent pattern mining , we review some related works on frequent pattern mining in this section.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, a natural question to ask is as follows: (1) For this high volume of social network data containing these ‘following’ relationships, can data mining techniques be applied? In response, over the past few years, several data mining algorithms and techniques have been proposed. Many of them are applicable to mine social networks (e.g., discovery of special events , detection of communities , subgraph mining , as well as discovery of popular friends , influential friends , and strong friends ).…”

Section: Introductionmentioning

confidence: 99%

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Parallel social network mining for interesting ‘following’ patterns

Leung

Jiang

Pazdor

et al. 2016

Concurrency and Computation

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Summary Social networking sites (e.g., Facebook, Google+, and Twitter) have become popular for sharing valuable knowledge and information among social entities (e.g., individual users and organizations), who are often linked by some interdependency such as friendship. As social networking sites keep growing, there are situations in which a user wants to find those frequently followed groups of social entities so that he can follow the same groups. In this article, we present (i) a space‐efficient bitwise data structure for capturing interdependency among social entities; (ii) a time‐efficient data mining algorithm that makes the best use of our proposed data structure for serial discovery of groups of frequently followed social entities; and (iii) another time‐efficient data mining algorithm for concurrent computation and discovery of groups of frequently followed social entities in parallel so as to handle high volumes of social network data. Evaluation results show the efficiency and practicality of our data structure and social network data mining algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel social network mining for interesting ‘following’ patterns

Leung

Jiang

Pazdor

et al. 2016

Concurrency and Computation

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“…RobustSpam also allows for deliberate introduction of uncertainty to protect patient confidentiality. Other examples of mining datasets with uncertainties can be found in [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints

Titarenko

Aivaliotis

et al. 2019

J Big Data

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Pattern mining is a powerful tool for analysing big datasets. Temporal datasets include time as an additional parameter. This leads to complexity in algorithmic formulation, and it can be challenging to process such data quickly and efficiently. In addition, errors or uncertainty can exist in the timestamps of data, for example in manually recorded health data. Sometimes we wish to find patterns only within a certain temporal range. In some cases real-time processing and decision-making may be desirable. All these issues increase algorithmic complexity, processing times and storage requirements. In addition, it may not be possible to store or process confidential data on public clusters or the cloud that can be accessed by many people. Hence it is desirable to optimise algorithms for standalone systems. In this paper we present an integrated approach which can be used to write efficient codes for pattern mining problems. The approach includes: (1) cleaning datasets with removal of infrequent events, (2) presenting a new scheme for time-series data storage, (3) exploiting the presence of prior information about a dataset when available, (4) utilising vectorisation and multicore parallelisation. We present two new algorithms, FARPAM (FAst Robust PAttern Mining) and FARPAMp (FARPAM with prior information about prior uncertainty, allowing faster searching). The algorithms are applicable to a wide range of temporal datasets. They implement a new formulation of the pattern searching function which reproduces and extends existing algorithms (such as SPAM and RobustSPAM), and allows for significantly faster calculation. The algorithms also include an option of temporal restrictions in patterns, which is available neither in SPAM nor in RobustSPAM. The searching algorithm is designed to be flexible for further possible extensions. The algorithms are coded in C++, and are highly optimised and parallelised for a modern standalone multicore workstation, thus avoiding security issues connected with transfers of confidential data onto clusters. FARPAM has been successfully tested on a publicly available weather dataset and on a confidential adult social care dataset, reproducing results obtained by previous algorithms in both cases. It has been profiled against the widely used SPAM algorithm (for sequential pattern mining) and RobustSPAM (developed for datasets with errors in time points). The algorithm outperforms SPAM by up to 20 times and RobustSPAM by up to 6000 times. In both cases the new algorithm has better scalability.

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“…Over the past two decades, numerous frequent itemset mining algorithms [8] have been proposed. While many of them are designed to mine precise data in which the existence of data items is certainly known, there is also demand for mining uncertain data (e.g., sensor network data, clinic reports) [9,14,15,19,30] in many other real-life applications. In these applications, each item is associated with an existential probability expressing the likelihood of the presence of that item.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Probabilistic Frequent Pattern Mining by PUH-Mine

Tong

Leung

Liu

et al. 2015

Web Technologies and Applications

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Abstract. To mine frequent itemsets from uncertain data, many existing algorithms rely on expected support based mining. An alternative approach relies on probabilistic based mining, which captures the frequentness probability. While the possible world semantics are widely used, the exponential growth of possible worlds makes the probabilistic based mining computationally challenging when compared to the expected support based mining. In this paper, we propose two efficient approximate hyperlinked structure based algorithms, which generate a collection of all potentially probabilistic frequent itemsets with a novel upper bound and verify if they are truly probabilistic frequent. Experimental results show the efficiency of our algorithms in mining probabilistic frequent itemsets from uncertain data.

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Mining constrained frequent itemsets from distributed uncertain data

Cited by 72 publications

References 34 publications

Parallel social network mining for interesting ‘following’ patterns

Parallel social network mining for interesting ‘following’ patterns

Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints

Probabilistic Frequent Pattern Mining by PUH-Mine

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