Data Mining and Knowledge Engineering

R. Saranya ¹, M. Jaikumar ²

Affiliations
1 Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore-641020, Tamilnadu, IN
2 Dept of Computer Applications (UG), Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore-641020, Tamilnadu, IN

Abstract

Determining road accident and provoke of the road occasion in every area is important for road safety enlargements. Earlier spatial immersion procedures did not concede the cause and asperity levels of road accidents. Applying Data-Driven Methods to ROAD Safety (DDMRS) can aid police departments designate resources more effectively. By lime-lighting on risky block, highly visible traffic law enforcement coincidently can diminish crashes. Most studies have focused on crunches after appealing new patrol paths, but few have archived how to progress or change police report time. To drastically reduce fatalities and serious cramps on roads, the ability need to review the appearance and cause of road accidents and classify the hidden information's behind the accidents using previous chronicles. For these analyses the raw data is not sufficient, so implementation of effective data excavating is obligatory.
With the use of data mining method such as decision trees will help to discover a best remedy for every scrutiny. The contemplated system familiarizes a road accident classification model. In the scheme, the system first mine associations rules of the crash data, and the detected rules will be build the decision tree named as "SDT" spatial Decision Tree, which is based on the consolidation of association rule and ID3 algorithms. Using these approaches the road accident data can be induced. And finally, the cause for the accident will be recognized for the desired effort.

Keywords

Road Accident Scrutiny, Methodology, SDT.

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M. Jaikumar ¹, R. Saranya ²

Affiliations
1 Dept. of Computer Applications (UG), Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore-641020, Tamilnadu, IN
2 Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore-641020, Tamilnadu, IN

Abstract

The cynosure of this paper is to fetch the important significance of Web Content Mining. The paper gives a vision into its functions, methods and its efforts in the current business environment as well in research extracting contents for day to day purpose. It also explicates how to use web content mining and how it plays an entity role by getting precious set of volumes and uses those contents in the judgment making in the cluster situation, literacy and scrutinize.

Keywords

Web Content Mining Techniques, Process of Mining.

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R. Saranya , R. Sathya , R. M. Umaiyal , P. Kanmani

Abstract

This paper will document the relationship between the consumer and their behaviors. Using this technique the consumers can use the web to find the information about the product and services. Ontologism can be constructed manually using ontology but the process can be tedious. The integration of knowledge acquisition with machine learning facilitates research toward automating the ontology generation process. Many approaches have been investigated for generating ontology. These include Natural Language Processing (NLP) techniques association rule mining hierarchical clustering translation from relational databases and Formal Concept Analysis. However these techniques focus mainly on constructing concept hierarchies from text documents or relational databases. They can also be used to find groups of people with similar interests. A major problem of traditional association rule mining techniques is that each item in a transaction is considered only to either exist or not. Thus, the user's preference and interest in each transaction item cannot be precisely represented. Since the concepts of preference and interest are fuzzy data fuzzy logic can be applied. For example combine fuzzy association rule mining and case-based reasoning (CBR) to improve the quality of web access pattern prediction. The fuzzy rule set was found to perform better in prediction accuracy and rule coverage than traditional rule set.

Keywords

Behavioral Tracking, Semantic Web, Knowledge Integration, Natural Language Processing.

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R. Saranya ¹, B. Arunkumar ¹

Affiliations
1 Karpagam University, IN

Abstract

Search queries on databases, and often it return a large number of results, only a small subset of result are relevant to the user. Ranking and categorization, which can also be combined, have been proposed to alleviate this information overload problem. Results categorization for databases is the focus of this work. In this paper, we present the system is a novel search interface that enables the user to navigate large number of query results by organizing them using the concept hierarchy. First, the query results are organized navigation tree. Inside the navigation tree edge cut operation is performed. The query results returns the two set of results that is relevant to the user and ignore results. At each node expansion step, this system of results reveals only a small subset of the concept nodes, selected such that the expected user navigation cost is minimized. In contrast, previous works expand the hierarchy in a predefined static manner, without navigation cost modelling. We show that the problem of selecting the best concepts to reveal at each node expansion and propose an efficient heuristic as well as a feasible optimal algorithm for relatively small trees.

Keywords

Information Retrieval, Navigation, Search Process.

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