International Journals

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    ANALYSIS OF VARIOUS DATA MINING TECHNIQUES TO PREDICT DIABETES MELLITUS
    (Research India Publications, 2016-01) J, Maria Shyla; M, Renuka Devi
    Data mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. This paper explores the early prediction of diabetes using various data mining techniques. The dataset has taken 768 instances from PIMA Indian Dataset to determine the accuracy of the data mining techniques in prediction. The analysis proves that Modified J48 Classifier provide the highest accuracy than other techniques.
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    A SURVEY OF COMMUNITY DETECTION IN ONLINE SOCIAL NETWORK
    (International Journal of Engineering Sciences & Research Technology, 2013-07) Padma Priya B; Sathiyakumari K
    In this paper we present a large Scale Community detection and analysis of Facebook, which gathers more than one billion active users in 2012. Characteristics of this online social network have been widely researched over these years. Facebook has affected the social life and activity of people in various ways. One major fact in today's technical world, people are very active users of Online Social Networks. They share every details of their day to day life and are in touch with their loved ones no matter in which part of the world they live. The impact is considerably taken into account as this online Social Network play a very important role in people lives. We study the structural properties of these samples in order to discover their community Structure. Here two Clustering algorithms are used to discover the communities in Complex networks and is compared.
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    UNSUPERVISED APPROACH FOR DOCUMENT CLUSTERING USING MODIFIED FUZZY C MEAN ALGORITHM
    (International Journal of Computer& Organization Trends, 2011-11) K, Sathiyakumari; V, Pream Sudha; G, Manimekalai
    Clustering is one the main area in data mining literature. There are various algorithms for clustering. There are several clustering approaches available in the literature to cluster the document. But most of the existing cluring techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. In recent times, inclusion of fuzzy logic in clustering results in better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. Before clustering, the documents are ranked using Term Frequency–Inverse Document Frequency (TF–IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering results when compared to the existing technique
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    BAT IMPERIALIST COMPETITIVE ALGORITHM (BICA) BASED FEATURE SELECTION AND GENETIC FUZZY BASED IMPROVED KERNEL SUPPORT VECTOR MACHINE(GF-IKSVM) CLASSIFIER FOR DIAGNOSIS OF CARDIOVASCULAR HEART DISEASE
    (Biomedical Research 2018; Special Issue: S95-S104, 2018) Nithya S; Suresh Gnana Dhas C
    Nowadays rate of death is increased due to the rapid growth of cardiovascular diseases. Due to the above reason, diagnosing the cardiovascular heart disease becomes very important in medical field. The subset features which are considered as vital role in disease diagnosis are identified for the same disease in modern medicine. Currently, many data mining techniques related to different types of heart disease diagnosis were presented by several authors. Existing methods mainly concentrated on high accuracy and less time consumption and it uses many different types of data mining techniques. This work consists of three major steps such as missing data imputation, high dimensionality reduction or feature selection and classification. The above steps are performed using a dataset called cardiovascular heart disease dataset with 500 patients and 14 features and it utilizes several effective features. Because of incomplete data collections Real time datasets often reveal unaware missing feature’s patterns. First step consists of the Expectation Maximization (EM) algorithm which fits an independent component model of the data. This increases the possibility of performing Modified Independent Component Analysis (MICA) on imperfect observations. Bat Imperialist Competitive Algorithm (BICA) based feature selection method is proposed to improve the dataset. BICA is an evolutionary algorithm which is based on the development of human's socio-political. In this algorithm an m number of features and the N number of cardiovascular heart disease observations are used as initial population which is called as countries. A classification approach is introduced with Genetic Fuzzy based Improved Kernel Support Vector machine (GF-IKSVM) classifier and a BICA based feature selection for the classification of cardiovascular heart disease dataset. BICA is used for feature selection methods to reduce number of features which indirectly decreases the important diagnosis tests required to the patients. The proposed method achieves 94.4% accuracy, which is higher than the methods used in the literature. This GFIKSVM classifier is well-organized and provides good accuracy results for cardiovascular heart diseasediagnosis.
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    CORONARY ARTERY DISEASE (CAD) PREDICITON AND CLASSIFICATION – A SURVEY
    (International Journal of Applied Engineering Research, 2015) S, Nithya; C, Suresh Gnana Dhas
    Data mining is the extraction of concealed prescient data from expansive databases furthermore a capable of new innovation with incredible potential to examine critical data in their data warehouses. Data mining algorithms anticipate future patterns and behaviors, permitting organizations to make proactive and knowledge driven choices. The computerized, forthcoming analyses offered by data mining move beyond investigations of past occasions gave by review tools commonplace of choice emotionally supportive networks. Data mining algorithms can answer business addresses that customarily were excessively prolonged to determine. They scour databases for concealed examples, discovering the prediction of disease that specialists may miss in light of the fact that it lies outside their desires. Manual checking is highly impossible to diagnose for this disease. To predict heart disease several approaches have been carried out. This comparative study paper provides a thorough analysis of various algorithms made towards heart disease prediction. Several data mining and soft computing approaches are studied. This study concludes that the performance of various algorithms comparison of accuracy, sensitivity and specificity of several algorithms and approaches.