Online ISSN: 2515-8260

Keywords : K-Means


Development of Top K-Association Rule Mining for Discovering pattern in Medical Dataset

Aakriti Sharma; Anjana Sangwan; Blessy Thankchan; Sachin Jain; Veenita Singh; Shantanu Saurabh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1413-1421

Association rules consist of the discovery of association between mining transaction items. This is one of the most important information mining jobs. It has been integrated into many commercial data mining software and has a wide variety of applications on a number of domains. So, computing the prediction rules in top rank data set is very difficult task. Finding the pattern in large data set require memory computational power high rate of I/O. and it is possible only on high computational machine. In this paper, selection of parameter which is used to compute is chosen based on minimum support and minimum confidence value. In this paper proposed a new algorithm which generates the association rule for the input parameters to finding the pattern in large data set. The algorithm starts searching the rules. As soon as a rule is found, it is added to the list of order rules list by support. The list is used so far to maintain top N rules found. Once valid rules are found, the minimum support for the internal minsup variable list is raised to support the rule. When the Minsup value is raised, the search space is robbed while searching for more rules. Then, every time a valid rule is found, the list is inserted into the list, the lists that are not listed in the list are excluded from the list and the minsup is raised for the price of the least fun rules in the list. Result shows that new method is efficient technique to mine data set from standard data with minimum configuration system.

A Novel Approach For Predicting Drug Response Similarity Using Machine Learning

M Supriya Menon; P Raja Rajeswari

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 796-808

Medical domain is revolutionized in terms of Diseases, Diagnosis, and Treatment Prediction thereby undergoing immense pressure due to the high dimensionality of numerable multivariate attributes, suppressing the quality of the analysis. Many techniques like Clustering and Classification have ruled over despite, rendering few hairline gaps towards attaining maximum efficiency. Our Machine Learning-based approach heads towards filling these gaps by adopting advanced K-Means in anticipating Drug likelihood in core attributes of Patients. The proposed Methodology focuses on determining Drug Response similarity by enhanced clustering technique concerning sensitive attributes of Patients. We successfully demonstrated its performance on the UCI Patient dataset reflecting enhanced results concerning Quality Parameters.

DETECTION AND CATEGORIZATION OF PLANT LEAF DISEASES USING NEURAL NETWORKS

V. Praveena; P. Chinnasamy; P. Muneeswari; R. Ananthakumar; Bensujitha .

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2438-2445

-Plants are very necessary for the earth and for all living organisms. Plants maintain the atmosphere. Plant illness, an impairment of the traditional state of a plant that interrupts or modifies its very important functions. All species of plants, wild and cultivated alike, are subject to illness. These diseases occur totally on leaves, but some might also occur on stems and fruits. Leaf diseases are the foremost common diseases of most plants. Plant pathology is the science study of pathogens and environmental circumstances causing illnesses in crops. Organisms causing transmissible disease include fungi, oomycetes, bacteria, viruses, viroids, etc. The latest technique involves automated classification of diseases from plant leaf images neural networks persecution approach called hunting enhancement of microorganisms primarily focused on executing Neural system relies on planar basic principle. Throughout this article, classic neural network algorithms are used to detect and classify the areas infected with multiple illnesses on the plant leaves in order to increase the velocity and precision of the network. The region's increasing formula will improve the network's potency by searching and grouping seed points with prevalent feature extraction method characteristics. The scheduled methodology achieves greater precision in disease detection and classification.

DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA

Arathi M; Govardhan A

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3378-3392

Time-Series Analysis exhibitedefficient results in delivering significant knowledge in numerous domains.
Most of the investigationon Time-Series Analysis is restrictedwith the
requirementofexpensivecategorized information. This led tothe growth of curiosity in groupingthe timeseries
informationthat does not need any access to categorized information. The clustering time-series
informationcarries out issues that donot prevail in conventional clustering methodologies.,in the
Euclidean space amongst the objects.Therefore,the authorsuggested an innovativeclustertechnique,
forTime-Seriesemploying of DTW similarity measure by extracting unsupervised shapelets. And these
extracted u-shapelets are clustered employing iterative k-means algorithm. The DTW similarity measure
provides better accuracy in formed clusters of proposed methodology compared tothe Metric
EuclidianDistance Measure. The performance of the suggested approach is evaluated employing theRand
Index (RI) Measure. The experimental for this approach was performed on four different Time-Series
data samples and the outcomes showed that the RI measure for the DTW based Time-Series Clustering
Algorithm is more when compared to the Existing ED-basedTime-Series Clustering Algorithm.