Frequent Approximate Subgraph Research Articles

Mining clique frequent approximate subgraphs from multi-graph collections

Recently, some algorithms have been reported for mining multi-graph Frequent Approximate Subgraphs (FASs). This kind of algorithm has a large applicability in social network analysis, image classification tasks and clustering, among others. However, all of them mine a large number of patterns. For this reason, some algorithms for mining a subset of FASs have been proposed. In this paper, we propose an efficient algorithm for mining a subset of FASs on multi-graph collections. Our proposed algorithm becomes an alternative for reducing the number of mined FASs by computing the clique FASs. It is important to highlight that, to the best of our knowledge; our proposal is the first algorithm for mining clique FASs on multi-graph collections. Our proposal is compared against other reported solutions and evaluated over several synthetic and real-world multi-graphs. In addition, we show the usefulness of the patterns mined by our proposal for image classification.

Extension of Canonical Adjacency Matrices for Frequent Approximate Subgraph Mining on Multi-Graph Collections

Into the data mining field, frequent approximate subgraph (FAS) mining has become an important technique with a broad spectrum of real-life applications. This fact is because several real-life phenomena can be modeled by graphs. In the literature, several algorithms have been reported for mining frequent approximate patterns on simple-graph collections; however, there are applications where more complex data structures, as multi-graphs, are needed for modeling the problem. But to the best of our knowledge, there is no FAS mining algorithm designed for dealing with multi-graphs. Therefore, in this paper, a canonical form (CF) for simple-graphs is extended to allow representing multi-graphs and a state-of-the-art algorithm for FAS mining is also extended for processing multi-graph collections by using the extended CF. Our experiments over different synthetic and real-world multi-graph collections show that the proposed algorithm has a good performance in terms of runtime and scalability. Additionally, we show the usefulness of the patterns computed by our algorithm in an image classification problem where images are represented as multi-graphs.

Graph modeling and mining methods for brain images

Brain disease is a top cause of death. Currently, its main diagonosis is to take advantage of medical brain images to analyse patients' condition. In medical big data analysis field, it has been a research hotspot that how to effectively represent medical images and discover significant information hidden in them to further assist doctors to achieve a better diagnosis. Graphs, as one of the most general forms of data representation, can easily represent entities, their attributes and their relationships well. However, the existing medical image graph models do not exploit the specific relationships of brain images very well so that some essential information is lost. Therefore, aiming at brain images, we firstly construct a domain knowledge-oriented graph about the Topological Relationships among Ventricles and Lesions (TRVL) to represent a brain image, and give the algorithm of modeling a brain Image to a TRVL Graph (denoted as I2G). Then we propose a method named Frequent Approximate Subgraph Mining based on Graph Edit Distance (FASMGED) to exactly discover meaningful patterns hidden in brain images. This method employs a strong error-tolerant graph matching strategy which is accordant with ubiquitous noise in practice. Moreover, an approximate method of frequent approximate subgraph mining is proposed based on the greedy strategy. We have evaluated our algorithms on real and simulated data. Results show that I2G is computationally scalable, FASMGED can discover more significant patterns than other state-of-the-art frequent subgraph mining methods, and the approximate method of frequent approximate subgraph mining outperforms FASMGED.

Improving graph-based image classification by using emerging patterns as attributes

In recent years, frequent approximate subgraph (FAS) mining has been used for image classification. However, using FASs leads to a high dimensional representation. In order to solve this problem, in this paper, we propose using emerging patterns for reducing the dimensionality of the image representation in this approach. Using our proposal, a dimensionality reduction over 50% of the original patterns is achieved, additionally, better classification results are obtained.

Representative Pattern Mining in Graph Collections

Nowadays, there has been a meaningful increase in the use of frequent approximate subgraph (FAS) mining for different applications, for example, graph classification. However, the great amount of mined patterns is one of the fundamental drawbacks of FAS mining. This drawback has a negative effect in the computational performance of classifiers, especially in large graph databases where the number of frequent patterns could be very high. In this paper, we propose a research proposal driven to obtain FAS mining algorithms capable to compute a representative subset of patterns. The representative pattern set should be identified into the mining process improving the efficiency in time, in comparison with the time required if this identification is performed in a post-processing stage over all patterns computed by a general FAS mining algorithm.

A new proposal for graph-based image classification using frequent approximate subgraphs

Graph-based data representations are an important research topic due to the suitability of this kind of data structure to model entities and the complex relations among them. In computer vision, graphs have been used to model images in order to add some high level information (relations) to the low-level representation of individual parts. How to deal with these representations for specific tasks is not easy due to the complexity of the data structure itself. In this paper we propose to use a graph mining technique for image classification, introducing approximate patterns discovery in the mining process in order to allow certain distortions in the data being modeled. We are proposing to combine a powerful graph-based image representation adapted to this specific task and frequent approximate subgraph (FAS) mining algorithms in order to classify images. In the case of image representation we are proposing to use more robust descriptors than our previous approach in this topic, and we also suggest a criterion to select the isomorphism threshold for the graph mining step. This proposal is tested in two well-known collections to show the improvement with respect to the previous related works.

Frequent approximate subgraphs as features for graph-based image classification

The use of approximate graph matching for frequent subgraph mining has been identified in different applications as a need. To meet this need, several algorithms have been developed, but there are applications where it has not been used yet, for example image classification. In this paper, a new algorithm for mining frequent connected subgraphs over undirected and labeled graph collections VEAM ( Vertex and Edge Approximate graph Miner) is presented. Slight variations of the data, keeping the topology of the graphs, are allowed in this algorithm. Approximate matching in existing algorithm (APGM) is only performed on vertex label set. In VEAM, the approximate matching between edge label set in frequent subgraph mining is included in the mining process. Also, a framework for graph-based image classification is introduced. The approximate method of VEAM was tested on an artificial image collection using a graph-based image representation proposed in this paper. The experimentation on this collection shows that our proposal gets better results than graph-based image classification using some algorithms reported in related work.