A STUDY ON GRAPH MINING ALGORITHMS TO DISCOVER FREQUENT SUBGRAPH PATTERNS FROM EXACT GRAPH DATA AND UNCERTAIN GRAPH DATABASE
International Journal of Latest Trends in Engineering and Technology · 2018
In Bioinformatics interaction between the proteins can be represented using the data structure called Graph.The objective behind going for data analysis is to obtain the various patterns from the available data sets in huge amount of data.It is considered as one of the way of mining the data.These extracted data patterns can be represented in the form of graph.There are various applications of graph mining.Graph Mining is helpful in mining the data of web browsing performed by net users,it is helpful in mining subsequences of DNA,in inferencing diagnostic rules from the stored patient history records.There are three graph mining techniques and each is having different way of mining the data from the database.They are graph clustering,graph classification and subgraph mining [2].A graph is used in data mining to represent data and used in many applications of Bioinformatics.The task of mining frequent patterns from the graph database is challenging as operation related to graph such as subgraph testing requires more time or high complexity task as comparison to the operations related to trees,sequences and so on [5].Graph mining is used to extract the information on the social media websites or social network.The communication on social network can be done through grouping,messaging, writing comments or by some other means like audio messaging ,emotion icons,and so on.