Weidi Dai

Tianjin University

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Research Field

Classification of debit card customers based on AHP and K-means

Chunxin Jiang Weidi Dai Wenjun Wang Yunqiang Jia

Debit card business is a very important business, bank debit card and bank users the most on the one hand, so every year the debit card user transaction data is very huge, in what is now the era of big data, data is wealth, we have huge debit card transaction data, you need to use big data technology to deal with the data analysis, found that there were a rule, and conducive to the development of bank data and results are obtained.In this article, through analysis of transaction data of debit card users, debit card users are classified, and the bank large customers and high quality clients and has great potential customers, and puts forward the time were significantly abnormal in the transaction data

Debit card

10.2991/mecs-17.2017.12

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Mutual Self Attention Recommendation with Gated Fusion Between Ratings and Reviews

Lecture notes in computer science (2020)

Qiyao Peng Hongtao Liu Yang Yu Hongyan Xu Weidi Dai

Attention network

10.1007/978-3-030-59419-0_33

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Citations (9)

A Local Computing-Based Hierarchical Clustering Algorithm Building Density Trees

Intelligent Control and Automation (2006)

Weidi Dai Jie-Liu Da-yi Zhao Zhenhua Liu Zhang Jun-xian

A new kind of clustering algorithm called LOCHDET (LOcal Computing-based Hierarchical clustering algorithm building DEnsity Trees) is presented in this paper. LOCHDET generates a density tree for each potential cluster according to its local density distribution. Each cluster is regarded as a tight coupling structure. Those "closer" clusters are merged if some conditio are satisfied. In order to reduce the cost time, a local computing technology is introduced. LOCHDET has a wide range of parameter settings, preferable accuracy in discovering clusters with arbitrary shape, good ability of processing noise data sets and weak sensitivity to input parameters by generalizing density-based, hierarchical, and locality-based methods. The results of our experiments confirm these mentioned above.

Hierarchical clustering

Tree (set theory)

10.1007/978-3-540-37256-1_80

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Citations (1)

Partition-Based Parallel Constructing-Density-Tree Clustering

Yunpeng Zhang Zhengjun Zhai Lu Zhang Yifei Bao Weidi Dai

A parallel constructing-density-tree clustering algorithm based on data partitioning (PCAP) was presented. The PCAP automatically partitioned global data space into load-balanced subspaces, which were distributed to different processors to complete subspacespsila clustering. The clustering result of global data space was achieved by merging those strong-association clusters though checking the association-intensity of leavespsila similarity. The detailed method of computing the association-intensity between clusters was described. Finally, the relevancy of the speedup and the amount of processors were discussed. The experiment results on artificial and real datasets show PCAP realizes the parallel of constructing-density-tree clustering algorithm and improves the clustering speed efficiently under preserving enough clustering precision. This approach is more suitable for dealing with great amounts of datasets.

Speedup

Tree (set theory)

Similarity (geometry)

10.1109/isip.2008.121

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CloudAssoc: A pipeline for imputation based genome wide association study on cloud

Weidi Dai Qiuwen Wang Meng Gao Lu Zhang

Genome wide association study (GWAS) has been proved to be an efficient approach to identify susceptibility genes for complex diseases. In order to increase the power for detecting the disease causal variants, imputation has been used to predict genotype dosages of untyped variants on the basis of linkage disequilibrium evaluated by public data. However, as the volume of data grows, time-consuming of imputation based association study becomes extremely large. We developed a cloud based pipeline to implement data format conversion, imputation, quality control and association study based on Map/Reduce framework which can aid biologists to accelerate the identification and evaluation of susceptibility genes for complex diseases and make it easier to combine GWAS data from worldwide for meta analysis.

Imputation (statistics)

Genome-wide Association Study

Linkage Disequilibrium

Genetic Association

10.1109/iccsnt.2012.6526190

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Citations (2)

A Fused Multi-feature Based Co-training Approach for Document Clustering

Yuanqing Wang Wenjun Wang Weidi Dai Pengfei Jiao Wei Yu

Document clustering is a popular topic in data mining and information retrieval. Most models and methods for this problem are based on computing the similarity between pair documents modeled in a space of all terms, or a new feature space obtained by applying a topic modeling technique for a given corpus. In this paper, we regard these two ideas as clustering on term feature and on semantic feature, and have an assumption that they can contribute to each other in clustering. Also, we propose a co-training approach for spectral clustering taking two features into account. Experiments on four real-world datasets show the feasibility and efficacy of our proposed approach compared with a number of the baseline methods.

Feature (linguistics)

Document Clustering

Feature vector

Spectral Clustering

Similarity (geometry)

Clustering high-dimensional data

Data stream clustering

10.1109/icisce.2016.19

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A Local Computing-Based Hierarchical Clustering Algorithm Building Density Trees

Lecture notes in control and information sciences (2006)

Weidi Dai Jie-Liu Da-yi Zhao Zhenhua Liu Junxian Zhang

Hierarchical clustering

Tree (set theory)

10.1007/11816492_80

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Citations (0)

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

International Journal of Modern Physics B (2016)

Tingting Wang Weidi Dai Pengfei Jiao Wenjun Wang

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

Snapshot (computer storage)

Dynamic network analysis

Stochastic block model

Evolving networks

Jaccard index

Social network (sociolinguistics)

10.1142/s0217979216500922

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Citations (10)

A framework of community detection based on individual labels in attribute networks

Physica A Statistical Mechanics and its Applications (2018)

Dong-Yang Nan Wei Yu Xiao Liu Yunpeng Zhang Weidi Dai

Non-negative Matrix Factorization

10.1016/j.physa.2018.08.100

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Citations (10)

A clustering algorithm based on building a density-tree

International Conference on Machine Learning and Cybernetics (2005)

Weidi Dai Yuexian Hou Pilian He Xiao-Shen Zheng

A new kind of clustering algorithm called CABDET is presented in this paper. CABDET creates a tree structure for every cluster, from which the neighbor's radius of the current object is calculated by the local density of its father node. Those unprocessed objects in the neighbor of the current object are added to extend the tree structure until no new object is founded. Each density-tree is regarded as one cluster. CABDET requires only one input parameter as the initial radius of the root node and has no limitation of density threshold. Other characteristics include the abilities of discovering clusters with arbitrary shape and processing the noise data. The result of our experiments demonstrates that CABDET is significantly more accurate in discovering density-changeable clustering than the algorithm DBSCAN, and that CABDET is less sensitive to input parameters.

DBSCAN

Tree (set theory)

Nearest-neighbor chain algorithm

10.1109/icmlc.2005.1527273

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Citations (3)