Multi-type clustering and classification from heterogeneous networks

Pio, Gianvito; Serafino, Francesco; Malerba, Donato; Ceci, Michelangelo

doi:10.1016/j.ins.2017.10.021

Cited by 42 publications

(18 citation statements)

References 40 publications

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“…For future work, we aim to introduce the possibility to handle mixed-types attributes (i.e., not only numerical attributes). Moreover, we plan to extend the proposed approach in order to make it able to solve classification tasks as well to measure and explicitly (8,16,24,32)…”

Section: Resultsmentioning

confidence: 99%

“…The peculiarities of data in such application domains further motivate the adoption of the predictive clustering framework in this paper. Indeed, not only several studies in the literature proved the effectiveness of predictive clustering frameworks [6,[14][15][16], but it has shown to be particularly appropriate when data exhibit different forms of autocorrelation [17], i.e., objects which are close to each other (spatially, temporally, or in a network) appear more related than distant objects. Such phenomena are commonly present in data regarding the cited domains and approaches based on clustering can naturally detect them.…”

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confidence: 99%

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DENCAST: distributed density-based clustering for multi-target regression

et al. 2019

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The generation of massive amounts of data in different forms (such as activity logs and sensor measurements) has increased the need for novel data mining algorithms, which are capable of building accurate models efficiently and in a distributed fashion. In recent years, several researchers proposed novel approaches to distribute the workload among several machines for classical clustering, classification and regression tasks [1]. However, only a few of them tackle the specific problem of density-based clustering. This problem has received much attention in the last decades, because of many desirable properties of the extracted clusters (arbitrarily-shaped, noise-free, robustness to outliers) which turn out the be useful in many application domains (e.g., spatial data analysis). Starting from the seminal work of DBSCAN [2], many algorithms have been proposed, but only a few of them are distributed. Unfortunately, existing distributed methods for density-based clustering suffer from several limitations. In particular, they are limited to data organized in a specific structure (e.g., they can analyze only low-dimensional feature spaces), or they suffer from overhead and scalability issues when the number of instances and attributes increase considerably [3-5]. These limitations depend from the

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

DENCAST: distributed density-based clustering for multi-target regression

et al. 2019

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“…Most of the previous studies focus on the networks' structure, and others use unlabeled objects for classification [15][16][17]. Collective classification [18] is one of the most popular classification methods in mining networks. These methods classify objects by their features and the structure of the network in the homogeneous information networks.…”

Section: Related Workmentioning

confidence: 99%

A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

Zhang

Jiang

et al. 2020

Applied Sciences

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There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.

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“…In addition, the heterogeneous network is explored to measure the relatedness of heterogeneous objects. Pio et al [31] introduced heterogeneous networks with an arbitrary structure to evaluate its performance for both clustering and classification tasks. Serafino et al [32] proposed an ensemble learning approach to classify objects of different classes, which is based on the heterogeneous networks for extracting both correlation and autocorrelation that involve the observed objects.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

2019

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Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.

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Multi-type clustering and classification from heterogeneous networks

Cited by 42 publications

References 40 publications

DENCAST: distributed density-based clustering for multi-target regression

DENCAST: distributed density-based clustering for multi-target regression

A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

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