rEMM: Extensible Markov Model for Data Stream Clustering in R [PDF]
Journal of Statistical Software, 2010 Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream
Michael Hahsler, Margaret H. Dunham
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Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
Applied SciencesSubspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems.
Wenjiang Shao, Xiaowei Zhang
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STRIDE: A Sequencing Depth Robust Metric for Sparse Chromosome Conformation Capture Data Comparison. [PDF]
Adv Sci (Weinh)A mean first passage time (MFPT)‐based representation transforms sparse Hi‐C contact maps into robust topological representations, preserving biologically meaningful chromatin features and facilitating robust analysis of TADs, chromatin loops, differential chromatin contacts, and sparse single‐cell Hi‐C data.
Xu B, Gao X, Tao W, Zhang Z, Li F.
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Research on HMM based link prediction method in heterogeneous network
Tongxin xuebao, 2022 In order to solve the problem that incomplete mining of structural information and semantic information in heterogeneous networks, a link prediction method combining meta-path-based analysis and hidden Markov model was proposed for link prediction of ...
Rong QIAN +4 more
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Deep Text Mining for Automatic Keyphrase Extraction from Text Documents
Journal of Intelligent Systems, 2011 Due to existence of a huge amount of textual data either on the World Wide Web or in textual databases like PubMed, the development of novel automatic keyphrase extraction methods has emerged as one of the key research problems in recent past ...
Abulaish Muhammad +2 more
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Dynamic order Markov model for categorical sequence clustering
Journal of Big Data, 2021 Markov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological dependencies hidden in sequential data.
Rongbo Chen +4 more
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A Hidden Markov Model-based approach to sequential data clustering [PDF]
, 2002 Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures.
BICEGO, Manuele +5 more
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Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
PLoS ONE, 2021 Cyber insurance is a risk management option to cover financial losses caused by cyberattacks. Researchers have focused their attention on cyber insurance during the last decade.
Yeftanus Antonio +2 more
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Operon Prediction Model Based on Markov Clustering Algorithm [PDF]
International Journal Bioautomation, 2019 There are many operon prediction models, but few methods can be applied to the operon prediction of new sequencing species effectively. In this paper, an operon prediction model based on Markov clustering algorithm is proposed.
Zhenmei Zhang, Yongquan Liang
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Compositional Approximate Markov Chain Aggregation for PEPA Models [PDF]
, 2012 Approximate Markov chain aggregation involves the construction of a smaller Markov chain that approximates the behaviour of a given chain. We discuss two different approaches to obtain a nearly optimal partition of the state-space, based on different ...
Dimitrios Milios +3 more
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