A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework.

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Large Scale Frequent Pattern Mining Using MPI One-Sided Model

2015 IEEE International Conference on Cluster Computing ◽

10.1109/cluster.2015.30 ◽

2015 ◽

Cited By ~ 3

Author(s):

Abhinav Vishnu ◽

Khushbu Agarwal

Keyword(s):

Large Scale ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern

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A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

Complexity ◽

10.1155/2018/2818251 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Dawen Xia ◽

Xiaonan Lu ◽

Huaqing Li ◽

Wendong Wang ◽

Yantao Li ◽

...

Keyword(s):

Big Data ◽

Association Analysis ◽

Intelligent Transportation Systems ◽

Large Scale ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Transportation Systems ◽

Frequent Pattern ◽

Trajectory Data ◽

Pattern Growth

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

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An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183894 ◽

2018 ◽

pp. 300-305

Keyword(s):

Information Sharing ◽

Pattern Mining ◽

Data Distribution ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

General Development ◽

Secure Information ◽

Evaluation Parameters ◽

Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

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Learning and Synchronized Privacy Preserving Frequent Pattern Mining

Journal of Software ◽

10.3724/sp.j.1001.2011.04000 ◽

2011 ◽

Vol 22 (8) ◽

pp. 1749-1760

Author(s):

Yu-Hong GUO ◽

Yun-Hai TONG ◽

Shi-Wei TANG ◽

Leng-Dong WU

Keyword(s):

Pattern Mining ◽

Frequent Pattern Mining ◽

Privacy Preserving ◽

Frequent Pattern

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RAKING: An Efficient K-Maximal Frequent Pattern Mining Algorithm on Uncertain Graph Database

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2010.01387 ◽

2010 ◽

Vol 33 (8) ◽

pp. 1387-1395 ◽

Cited By ~ 4

Author(s):

Meng HAN ◽

Wei ZHANG ◽

Jian-Zhong LI

Keyword(s):

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Graph Database ◽

Uncertain Graph ◽

Mining Algorithm ◽

Maximal Frequent Pattern

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Sliding window based weighted maximal frequent pattern mining over data streams

Expert Systems with Applications ◽

10.1016/j.eswa.2013.07.094 ◽

2014 ◽

Vol 41 (2) ◽

pp. 694-708 ◽

Cited By ~ 64

Author(s):

Gangin Lee ◽

Unil Yun ◽

Keun Ho Ryu

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern ◽

Maximal Frequent Pattern

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Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams

2021 7th International Conference on Optimization and Applications (ICOA) ◽

10.1109/icoa51614.2021.9442621 ◽

2021 ◽

Author(s):

G. Suseendran ◽

D. Balaganesh ◽

D. Akila ◽

Souvik Pal

Keyword(s):

Deep Learning ◽

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Structured Data ◽

Frequent Pattern ◽

Complex Data ◽

Fast Speed

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Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Genes ◽

10.3390/genes12081160 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1160

Author(s):

Atsuko Okazaki ◽

Sukanya Horpaopan ◽

Qingrun Zhang ◽

Matthew Randesi ◽

Jurg Ott

Keyword(s):

Null Hypothesis ◽

Pattern Mining ◽

Genetic Diseases ◽

Frequent Pattern Mining ◽

Case Control ◽

Frequent Pattern ◽

Permutation Testing ◽

Case Control Studies ◽

P Values ◽

Dna Variants

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

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