06-02-2013, 11:15 AM
A LINK-ANALYSIS EXTENSION OF CORRESPONDENCE ANALYSIS FOR MINING RELATIONAL DATABASES
A LINK-ANALYSIS EXTENSION.docx (Size: 14.53 KB / Downloads: 19)
Functional Requirements
1. Registration
2. Notations and definitions
3. Diffusion-map distance
4. Analyzing Relations
5. Analyzing The Reduced Markov Chain With The Basic Diffusion Map
Registration
In this Module Members are registered his/her personal, educational, family, business details. In this project to maintainpopulation. So, each & every person fulfills the details.
Notations and definitions
In this module, we given a weighted, directed, graph G possibly defined from a relational database in the following, obvious, way: each element of the database is a node and each relation corresponds to a link for a detailed procedure allowing to build a graph from a relational database.
Diffusion-map distance
In our two-step procedure, a diffusion map projection, based on the so-called diffusion map distance, will be performed after stochastic complementation. Now, since the original definition of the diffusion map distance deals only with undirected, a periodic, Markov chains, it will first be assumed in that the reduced Markov chain, obtained after stochastic complementation, is indeed undirected, a periodic, and connected in which case the corresponding random walk defines an irreducible reversible Markov chain. Notice, that it is not required that the original adjacency matrix is irreducible and reversible; these assumptions are only required for the reduced adjacency matrix obtained after stochastic.
Analyzing Relations
In this section, the concept of stochastic complementation is briefly reviewed and applied to the analysis of a graph through the random-walk-on-a-graph model. From the initial graph, a reduced graph containing only the nodes of interest, and which is much easier to analyze, is built.
Analyzing the Reduced Markov Chain with the Basic Diffusion Map
Once a reduced Markov chain containing only the nodes of interest has been obtained, one may want to visualize the graph in a low-dimensional space preserving as accurately as possible the proximity between the nodes. This is the second step of our procedure. For this purpose, we propose to use the diffusion maps, computing a basic diffusion map on the reduced Markov chain is equivalent to correspondence analysis in two special cases of interest: a bipartite graph and a star schema database. Therefore, the proposed two-step procedure can be considered as a generalization of correspondence analysis.
a larger number of categorical variables
Non-Functional Requirements
Non functional requirements describe user-visible aspects of the system that are not directly related to functionality of the system.
Documentation
The client is provided with an introductory help about the client interface and the user documentation has been developed through help menu.
User Interface
A interface has been provided to the client that is compatible with Windows environment and is designed to be user friendly.
Error Handling
The operations of NU and LSU are too frequent, the power and communication band width of nodes are wasted for those unnecessary updates. On the other hand, if the frequency of the operations of NU and/or LSU is not sufficient, the location error will degrade the performance of the application that relies on the location information of nodes.
Design Constraints
This location information not only provides one more degree of freedom in designing network protocols, but also is critical for the success of many military and civilian applications, e.g., localization in future battlefield networks and public safety communications.
Acceptance Criteria
The tradeoff between the operation costs in location updates and the performance losses of the target application due to location in accuracies (i.e., application costs) imposes a crucial question for nodes to decide the optimal strategy to update their location information, where the optimality is in the sense of minimizing the overall costs.