1 / 1

Interactive Exploration of Parameter Space for Data Mining

This project, supported by NSF grants, aims to retrieve a precise number of interesting rules from a large continuous parameter space by summarizing it for exploratory data mining. Analysts can explore association rules through an interactive approach with support for redundant and repeated rules. The technique involves constructing a rule graph and utilizing a full lattice, a reduced lattice, and an index structure for efficient rule retrieval and storage. The ultimate goal is to achieve store-n-reuse capability for quick response to user queries and to define stable regions in the parameter space where no new association rules are formed despite changing parameters.

dagan
Download Presentation

Interactive Exploration of Parameter Space for Data Mining

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Abhishek Mukherji, Xika Lin, Professor Elke A. Rundensteiner, Professor Matthew O. Ward XMDVTool, Department of Computer Science This project is supported by NSF under grantsIIS-080812027 andCCF-0811510. Confidence INTERACTIVE AND EXPLORATORY DATA MINING SUMMARIZING PARAMETER SPACE GOAL: Retrieve the right number of interesting rules. Large continuous parameter space Support Redundant rules: AB=>C | A=>B | A=>BC minSupp minConf • Patterns are non-uniformly distributed over the data set. • No prior knowledge of how new rules will be generated with change in parameter values. • Analysts proceed by trial-and-error. Repeated rules: Once valid, rule X=>Y will remain valid for the entire subspace. Summarizing Parameter Space for Interactive Exploration of Association Rules Data Miner • Cumbersome to store rules for potentially infinite number of threshold pairs. • Redundant and repeated rules may clutter users understanding. {ARs}  Limitations • Long response time. • No reuse of results. Can we store-n-reuse? PARAMETER SPACE CONSTRUCTION OVERALL TECHNIQUE RULE GRAPH SEARCH Confidence Full lattice representing a dataset Reduced lattice* 1. Determine all cut-points in the parameter space. 2. Populate each block with rules. (12345) D 1. Determine all cut-points in the parameter space. 2. Populate each block with association rules. A=>BCD S = 3, C = 3/4 Support • Redundancy eliminating search over a directed acyclic graph. support list • itemset C->B A->BD D->B 1 1 B->D CONTRIBUTIONS INDEX STRUCTURE 5/6 5/6 • Confidence • Confidence • Explored the parameter space for ARs. • Defined stable regions in the parameter space. • Developed efficient index and search mechanisms. • Achieved store-n-reuse for quick response to interactive user queries . 4/5 4/5 Stable region: NO new ARs are produced despite change in parameters. • 1. Eliminate repeated rules • Each rule is only stored once. D->AB 3/4 3/4 4/6 4/6 2. Create 2-level search tree. B->C B->AD 3/5 3/5 3/6 3/6 0 0 • Support 0 0 3 3 4 4 5 5 • Support *Mohammed J. Zaki. Mining non-redundant association rules. Data Mining Knowledge Discovery, 9(3):223-248, 2004

More Related