Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data

Xu, Peng; Deng, Zhaohong; Choi, Kup‐Sze; Cao, Longbing; Wang, Shitong

doi:10.1609/aaai.v33i01.3301379

Cited by 21 publications

(2 citation statements)

References 18 publications

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“…Bloechl et al [54] combined ITCC with Markov chain polymerization to improve its performance. Xu et al [51] combined multiple views with ITCC, so that views share the same clustering results in the sample dimension and have different clustering results in the feature dimension, and used the maximum entropy mechanism to control the importance of different views.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy weighted multi-kernel subspace co-clustering based on structural-α entropy

Zhang

2022

Preprint

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Co-clustering, which combines the rows and columns of data matrix, has achieved widespread success in many application fields. For further improving performance of clustering algorithm, we introduce the structural-α entropy of weighting vector of the co-clustering model, constructing a structural-α entropy weighting subspace clustering model, theoretically derived structural-α entropy weighting subspace two-way subspace clustering algorithm STWCC. This algorithm not only can discover clusters in different subspaces but also discover the relationship between samples and features of different subspaces in the data space, better revealing the relations between samples and features of data by maximizing the weighting entropy. On this basis, the fuzzy weighted kernel space joint clustering model of structural-α entropy is obtained by introducing fuzzy entropy of membership. By introducing the kernel method, the weighted kernel space joint clustering model of structural-α entropy is obtained. By introducing the autoencoder, the weighted subspace co-clustering model of structural-α entropy of auto-encoder is obtained, and the corresponding algorithm is derived. The proposed algorithm is experimented with synthetic datasets, and real datasets including UCI standard datasets, and is compared with some typical clustering algorithms. The results show that the proposed algorithm has a certain improvement in performance.

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Section: Related Workmentioning

confidence: 99%

Fuzzy weighted multi-kernel subspace co-clustering based on structural-α entropy

Zhang

2022

Preprint

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“…CENTRIST, LBP, GIST, and HOG features were extracted as four views for experiments. 6) Leavers [48]: a plant image dataset containing 100 categories of plants. Shape, Texture and Margin features were extracted as three views for experiments.…”

Section: Mvrl_fsmentioning

confidence: 99%

Vertical extension to the Hong Kong Polytechnic University

Richard.¹,

莊志量.²

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Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes PROPHET, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for preexecuted results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. PROPHET orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that PROPHET improves the throughput by 3.11× and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding.

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