Contact at mumbai.academics@gmail.com or 8097636691
Responsive Ads Here

Thursday, 22 February 2018

Knowledge-Based Interactive Postmining of Association Rules Using Ontologies(2010)


Knowledge-Based Interactive Postmining of 

Association Rules Using Ontologies(2010)

ABSTRACT
In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and postprocessing. Thus, it is crucial to help the decision-maker with an efficient postprocessing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose to use ontologies in order to improve the integration of user knowledge in the postprocessing task. Second, we propose the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach over voluminous sets of rules, we were able, by integrating domain expert knowledge in the postprocessing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process.
Existing System:
In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. It is very well known that miningalgorithms can discover a prohibitive amount of association\rules; for instance, thousands of rules are extracted from adatabase of several dozens of attributes and several hundreds of transactions. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and postprocessing. However, being generally based on statistical information
Disadvantages or Demerits of Existing System:
As a result, it is necessary to bring the support thresholdlow enough in order to extract valuable informationUnfortunately, the lower the support is, the larger thevolume of rules becomes, making it intractable for a decision-maker to analyze the mining result. Experiments show that rules become almost impossible to use when the number of rules overpasses 100. Thus, it is crucial to help the decision-maker with an efficient technique for reducing the number of rules
Proposed System:
This paper proposes a new interactive postprocessing approach, ARIPSO (Association Rule Interactive post-Processing using Schemas and Ontologies) to prune and filter discovered rules. First, we propose to use Domain Ontologies in order to strengthen the integration of user knowledge in the postprocessing task. Second, we introduce Rule Schema formalism by extending the specification language proposed by Liu et al.  for user beliefs and expectations toward the use of ontology concepts. Furthermore, an interactive and iterative framework is designed to assist the user throughout the analyzing task. The interactivity of our approach relies on a set of rule mining operators defined over the Rule Schemas in order to describe the actions that the user can perform.
Advantages or Merits of Proposed System:
iterative framework is designed to assist the user throughout the analyzing task. The interactivity of our approach relies on a set of rule mining operators defined over the Rule Schemas in order to describe the actions that the user can perform.
Modules:
1. Design of   Dataset
2. Clustering process
3. Distance-based projected clustering algorithm
MODULES DESCRIPTION:
1. Design of   Dataset
Create  dataset which has the datas like  location, server id and service. Assign constraints to the columns in tbe dataset.
These constraints are used to avoid the duplicate rows in the table. Here  these use constraints like Not Null and primarykey.
2. Clustering process
The clustering process is based on the k-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense. Our algorithm is capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in the full-dimensional space. The suitability of our proposal has been demonstrated through an empirical study using synthetic and real datasets
3. Distance-based projected clustering algorithm
The algorithm consists of three phases. The first phase performs attribute relevance analysis by detecting dense and sparse regions and their location in each attribute. Starting from the results of the first phase, the goal of the second phase is to eliminate outliers, while the third phase aims to discover clusters in different subspaces.
SOFTWARE REQUIREMENTS
  Operating system          : Windows XP Professional
  Front End                : Microsoft Visual Studio .Net  2005
  Coding Language       : Visual C# .Net,ASP.NET2.0
  Back End                       :  SqlServer 2000
 HARDWARE REQUIREMENTS
  SYSTEM             : Pentium IV 2.4 GHz
   HARD DISK        : 40 GB
   FLOPPY DRIVE       : 1.44 MB
   MONITOR           : 15 VGA colour
   MOUSE               : Logitech.
   RAM                    : 256 MB
   KEYBOARD       : 110 keys enhanced.

No comments:

Post a Comment