C: Software & Data Engineering (217)
GJCST: C
Semantic Clustering of Genomic Documents using GO Terms as Feature Set
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 10 The biological databases generate huge volume of genomics and proteomics data. The sequence information is used by researches to find similarity of genes, proteins and to find other related information. The genomic sequence database consists of large number of attributes as annotations, represented for defining the sequences in Xml format. It is necessary to have proper mechanism to grou...
Paper Information
Authors Dr. B.L.Shivakumar, V.BhuvaneswariRead Journal Click Here
Read Paper Click Here
Page No. 13 to 19
Data Mining Based on Semantic Similarity to mine new Association Rules
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 12 The problem of mining association rules in a database are introduced. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. A new algorithm called “Improved Data Mining Based on Semantic Similarity to mine new Association Rules†which considers not only exact matches between items, bu...
Paper Information
Authors Dr. Sandeep Jain, Aakanksha MahajanRead Journal Click Here
Read Paper Click Here
Page No. 11 to 17
Intelligent Information Retrieval
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 12 The World Wide Web has become an invaluable information resource but the explosion of information available via the web has made web search a time consuming and complex process. Index-based search engines, such as AltaVista, Google or Info seek help, but they are not enough. This paper describes the rationale, architecture, and implementation of a next generation information gathering sy...
Paper Information
Authors Dr. Ayushi Gupta, Divya Verma, Kajal GambhirRead Journal Click Here
Read Paper Click Here
Page No. 9 to 10
Understanding Rule Behavior through Apriori Algorithm over Social Network Data
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 10 APRIORI algorithm is a popular data mining technique used for extracting hidden patterns from data. This paper highlights practical demonstration of this algorithm for association rule mining over a survey data set of students related to social network usage. We concluded with discussions on the number of research observations including new rules generated during the process. ...
Paper Information
Authors Dr. S.S.Phulari , P.U.Bhalchandra, Dr.S.D.Khamitkar, S.N.LokhandeRead Journal Click Here
Read Paper Click Here
Page No. 9 to 12
Knowledgebase Representation for Royal Bengal Tiger In The Context of Bangladesh
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 10 Royal Bengal Tiger is one of the penetrating threaten animal in Bangladesh forest at Sundarbans. In this work we have had concentrate to establish a robust Knowledgebase for Royal Bengal Tiger. We improve our previous work to achieve efficiency on knowledgebase representation. We have categorized the tigers from others animal from collected data by using Support Vector Machines(SVM) .Man...
Paper Information
Authors Dr. Md.Sarwar Kamal, Sonia Farhana NimmyRead Journal Click Here
Read Paper Click Here
Page No. 1 to 8
Data Mining in Clinical Practices Guidelines
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 12 This paper proposes text mining of clinical practices to extract decision-making steps. These steps should be formed in- logical functions capable of branching on different plan set on some deciding variables. The probable action sequence will be notified on the data of patient given to the conditions of clinical guideline and this will also give critical conditions that need immediate a...
Paper Information
Authors Dr. Prof. Mayura KinikarRead Journal Click Here
Read Paper Click Here
Page No. 5 to 8
Query Join Processing over Uncertain Data for Decision Tree Classifiers
Published In: GJCST Part: C: Software & Data Engineering Volume: 12 Issue: 12 Traditional decision tree classifiers work with the data whose values are known and precise. We can also extend those classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty measurement/quantization errors, data staleness, and multiple repeated measurements. Rather than abstrac...
Paper Information
Authors Dr. V. Yaswanth Kumar , G. KalyaniRead Journal Click Here
Read Paper Click Here
Page No. 19 to 22