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Tengfei Zhang

Bio: Tengfei Zhang is an academic researcher. The author has contributed to research in topics: Canopy clustering algorithm & k-medians clustering. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Journal ArticleDOI
TL;DR: An improved algorithm of rough k-means clustering based on variable weighted distance measure is presented and Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.
Abstract: Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cluster centers when computing new mean for each cluster. To solve this issue, an improved algorithm of rough k-means clustering based on variable weighted distance measure is presented in this article. Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.

2 citations


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Book ChapterDOI
01 Jan 2017
TL;DR: The review starts with RST in the context of data preprocessing as well as the generation of both descriptive and predictive knowledge via decision rule induction, association rule mining and clustering.
Abstract: This chapter emphasizes on the role played by rough set theory (RST) within the broad field of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm, RST has much to offer to the ML community. We surveyed the existing literature and reported on the most relevant RST theoretical developments and applications in this area. The review starts with RST in the context of data preprocessing (discretization, feature selection, instance selection and meta-learning) as well as the generation of both descriptive and predictive knowledge via decision rule induction, association rule mining and clustering. Afterward, we examined several special ML scenarios in which RST has been recently introduced, such as imbalanced classification, multi-label classification, dynamic/incremental learning, Big Data analysis and cost-sensitive learning.

31 citations

Journal ArticleDOI
TL;DR: The sparse subspace clustering (SSC) algorithm is introduced to analyze the time series data and has a better performance both on the artificial data set and the daily box-office data than recently developed well-known clustering algorithm such as K-means and spectral clustering algorithm.
Abstract: Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task Our study focuses on finding the regular box-office revenue patterns Clustering algorithm is unsupervised machine learning algorithm which classifies the data in the absence of early knowledge of the classes Unlike static data, the time series data vary with time The work focused on time series clustering analysis is relatively less than those focused on static data In this paper, the sparse subspace clustering (SSC) algorithm is introduced to analyze the time series data The SSC algorithm has a better performance both on the artificial data set and the daily box-office data than recently developed well-known clustering algorithm such as K-means and spectral clustering algorithm On the artificial data set, SSC is more suitable for time series, whether from the angle of clustering error or visualization On the actual data, movies are divided into five clusters by SSC algorithm, and each cluster represents a distinct type of distribution pattern And these patterns can be used in movie recommendation, film evaluation and can guide theater exhibitors and distributors In addition, this is the first time to apply SSC to deal with time series clustering problem and get a pleasant effect

12 citations