Data Mining: Graph mining and social network analysis
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Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
Data Mining: Graph mining and social network analysis
- 1. Graph Mining, Social Network Analysis, and Multi relational Data Mining
- 2. Why and What is Graph Mining?Graphs become increasingly important in modeling complicated structures, such as circuits, images, biological networks, social networks, the Web, and XML documents. Many graph search algorithms have been developed in chemical informatics, computer vision, video indexing, and text retrieval. 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.
- 3. Methods for Mining Frequent Sub graphsApriori-based ApproachApriori-based algorithms for frequent substructure mining include AGM, FSG, and a path-join method. AGM shares similar characteristics with Apriori-based item-set mining. FSG and the path-join method explore edges and connections in an Apriori-based fashion.
- 4. Other Approach for Mining Frequent Sub graphs Pattern Growth Graph Approach : Simplistic pattern growth-based frequent substructure mining.gSpan: A pattern-growth algorithm for frequent substructure mining. (for detailed algorithm refer wiki)
- 5. Characteristics of Social NetworksDensification power lawShrinking diameterHeavy-tailed out-degree and in-degree distributions
- 6. Link MiningTraditional 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. The data comprising social networks tend to be heterogeneous, multi relational, and semi-structured. As a result, a new field of research has emerged called link mining.
- 7. Tasks involved in link mining Link-based object classification.Object type prediction.Link type prediction.Predicting link existenceLink cardinality estimation.Object reconciliation.Group detectionSub graph detectionMetadata mining
- 8. Challenges faced by Link MiningLogical versus statistical dependenciesFeature constructionInstances versus classes.Collective classification and collective consolidation.Effective use of labeled and unlabeled dataLink predictionClosed versus open world assumptionCommunity mining from multi relational networks.
- 9. What is Multi relational Data Mining?Multi relational data mining (MRDM) methods search for patterns that involve multiple tables (relations) from a relational database
- 10. Multi relational Clustering with User GuidanceMulti relational clustering is the process of partitioning data objects into a set of clusters based on their similarity, utilizing information in multiple relations.
- 11. Visit more self help tutorialsPick a tutorial of your choice and browse through it at your own pace.The tutorials section is free, self-guiding and will not involve any additional support.Visit us at www.dataminingtools.net