[PDF] Top 20 A Survey of Frequent Subgraph Mining algorithms for Uncertain Graph Data
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A Survey of Frequent Subgraph Mining algorithms for Uncertain Graph Data
... To avoid costly BFS, adopt DFS, where patterns are grown directly from a single graph Instead of joining two previous subgraphs. gspan is the main representative of this pattern growth. It uses tree representation ... See full document
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A survey of frequent subgraph mining algorithms
... FGM algorithms use an approximate measure to compare the similarity of two graphs, ...a subgraph may contribute to the support count for a candidate subgraph if it is in some sense similar to the ... See full document
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1. A study on graph mining algorithms to discover frequent subgraph patterns from exact graph data and uncertain graph database
... apriori-based frequent subgraph mining algorithms exist,they are AGM,FSG and Edge-disjoint Path-join ...a graph ,then this resultant graph is checked to see whether it is ... See full document
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On Graph Perturbation Theory and Algorithms for Scalable Mining of Noisy and Uncertain Graph Data with Knowledge Priors.
... different algorithms for enumerating the maximal cliques of a ...MCE algorithms, was developed by Bron and Kerbosch ...A survey by Johnston [42] indicated that BK performed favorably against a set of ... See full document
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Frequent and Significant Patterns Mining Using Approximate Patterns for Protein Structure Analysis
... existing algorithms that mine frequent and significant patterns and find use in ...popular graph mining algorithms based on graph theory are proposed such as AGM (Apriori-based ... See full document
9
A Survey on Contribution of Data Mining Techniques and Graph Reading Algorithms in Concept Map Generation
... this survey paper depicts that using different mining algorithms one can automatically generate concept map for any big ...using frequent mining algorithms and graph ... See full document
7
Mining Frequent Subgraph by Incidence Matrix Normalization
... Abstract—Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain well- labeled vertices and ... See full document
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Survey on Graph Pattern Mining Approach
... is frequent if and only if all of its sub-item sets are ...that frequent item sets can be mined by first scanning the database to find the frequent 1-itemsets, then using the frequent ... See full document
5
Hybrid algorithms for subgraph pattern queries in graph databases
... with graph-ID lists; ...the graph-ID lists is performed to form the candidate ...query graph is tested for sub-iso against each graph in the candidate set to produce the final answer ...the ... See full document
11
An iterative mapreduce based frequent subgraph mining algorithm with load balancing
... graph mining algorithms silently assume that the graph fits in the memory of a typical workstation, or at least on a single disk; the above graphs violate these assumptions, spanning multiple ... See full document
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Frequent Subgraph Mining for Graph based Tamil Bibliographic Big Data Analytics
... Abstract: Data analysis can be done in more effectively, when they are represented in the form of ...Especially, Frequent Subgraph Mining (FSM) is an important technique for extracting similar ... See full document
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Frequent Pattern Mining Algorithms PrePost, FIN, H-Mine – A Survey
... basic algorithms in the frequent pattern ...the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the ... See full document
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Mining Frequent Patterns in Uncertain and Relational Data Streams using the Landmark Windows
... inconsistent data in a data ...introduced algorithms; Evaluations show that among the four proposed algorithms, LUF-streaming algorithm which uses the landmark window model, and TUF- streaming ... See full document
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Efficient Tree Based Distributed Data Mining Algorithms for mining Frequent Patterns
... Mining frequent patterns creates a boon for market research analysis to predict the customer behavioural patterns, since the source of data have been distributed and dynamic in nature the ... See full document
6
A Novel Frequent Subgraph Mining Based Intelligent MapReducing Technique
... "Large-scale frequent subgraph mining in Mapreduce", in that they clearly described such as Mining continuous subgraphs from a substantial gathering of chart articles is a vital issue ... See full document
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Frequent Subgraph Mining Based Collaboration Pattern Analysis for Wikipedia
... set data, in which the numbers of “FA” and “GA” articles are both 16 and the numbers of the remaining four classes of articles are all ...of frequent subgraphs which can also be regarded as complex ... See full document
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Survey on Big Data Mining Algorithms
... large data sets In order to meet the growing demands of data, we need to scale up both in terms of capacity and performance measures ...Big data needs more capacity, scalability, and efficient ... See full document
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A Comparative study on data mining clustering...
... the data such that there is a higher intra-cluster similarity and lower inter-cluster ...the data in the form of clusters. Partitioning the data recursively and generating clusters by using top down ... See full document
5
Classification Algorithms in Data Mining : A Survey
... Data mining is the pattern of examining large pre- existing database in order to generate new ...from data mining is hopefully some new and ...the data mining process is to ... See full document
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Frequent Contiguous Pattern Mining Algorithms for Biological Data Sequences
... All the above algorithms either generate spanning tree for the base patterns or create tables with location information for base patterns in the DB. They optimize sorting and joining methods to reduce the search ... See full document
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