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Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
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Data Mining: Graph mining and social network analysis

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Data Mining: Graph mining and social network analysis

Data Mining: Graph mining and social network analysis

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  • 1. Graph Mining, Social Network Analysis, and Multi relational Data Mining<br />
  • 2. Why and What is Graph Mining?<br />Graphs become increasingly important in modeling complicated structures, such as circuits, images, biological networks, social networks, the Web, and XML documents.<br /> Many graph search algorithms have been developed in chemical informatics, computer vision, video indexing, and text retrieval.<br /> With the increasing demand on the analysis of large amounts of structured data, graph mining has become an active and important theme in data mining.<br />
  • 3. Methods for Mining Frequent Sub graphs<br />Apriori-based Approach<br />Apriori-based algorithms for frequent substructure mining include AGM, FSG, and a path-join method.<br /> AGM shares similar characteristics with Apriori-based item-set mining. <br />FSG and the path-join method explore edges and connections in an Apriori-based fashion.<br />
  • 4. Other Approach for Mining Frequent Sub graphs <br />Pattern Growth Graph Approach : Simplistic pattern growth-based frequent substructure mining.<br />gSpan: A pattern-growth algorithm for frequent substructure mining.<br /> (for detailed algorithm refer wiki)<br />
  • 5. Characteristics of Social Networks<br />Densification power law<br />Shrinking diameter<br />Heavy-tailed out-degree and in-degree distributions<br />
  • 6. Link Mining<br />Traditional methods of machine learning and data mining, taking, as input, a random sample of homogenous objects from a single relation, may not be appropriate in social networks. <br />The data comprising social networks tend to be heterogeneous, multi relational, and semi-structured. <br />As a result, a new field of research has emerged called link mining.<br />
  • 7. Tasks involved in link mining <br />Link-based object classification.<br />Object type prediction.<br />Link type prediction.<br />Predicting link existence<br />Link cardinality estimation.<br />Object reconciliation.<br />Group detection<br />Sub graph detection<br />Metadata mining<br />
  • 8. Challenges faced by Link Mining<br />Logical versus statistical dependencies<br />Feature construction<br />Instances versus classes.<br />Collective classification and collective consolidation.<br />Effective use of labeled and unlabeled data<br />Link prediction<br />Closed versus open world assumption<br />Community mining from multi relational networks.<br />
  • 9. What is Multi relational Data Mining?<br />Multi relational data mining (MRDM) methods search for patterns that involve multiple tables (relations) from a relational database<br />
  • 10. Multi relational Clustering with User Guidance<br />Multi relational clustering is the process of partitioning data objects into a set of clusters based on their similarity, utilizing information in multiple relations. <br />
  • 11. Visit more self help tutorials<br />Pick a tutorial of your choice and browse through it at your own pace.<br />The tutorials section is free, self-guiding and will not involve any additional support.<br />Visit us at www.dataminingtools.net<br />

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