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IJCRR - 8(4), February, 2016

Pages: 34-38

Date of Publication: 21-Feb-2016

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A STUDY ON DATA MINING TECHNIQUES, METHODS, TOOLS AND APPLICATIONS IN VARIOUS INDUSTRIES

Author: R. Jaya, S. Mohan Kumar

Category: Healthcare

Abstract:Data Mining is special and important technique utilized to have better business solutions. General survey on Data Mining Techniques, Methods, Tools and Challenges of Data Mining in all the domains, is at most important and so much in demand, applying machine learning concepts and techniques in medical field is also most essential in the present scenario. In this paper the following chapters are presented- various data mining techniques, merits and demerits of data mining, open source data mining tools available, and also domains or industries applied DM.

Keywords: Data mining, DM tools, HealthCare software, DM techniques, Classification, Clustering, Decision tree, DM challenges

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INTRODUCTION The survey conduction and publishing the survey result is most expected one, in this research report. DM Techniques and how much the DM technique contributed in the healthcare field [14] is presented.

II. TYPES OF DM TECHNIQUES: 1. CLASSIFICATION: It is important DM Technique; Classification predicts a certain output based on a set of pre classified examples and it is the mostly used data mining technique. Classification can be broadly divided into supervised and unsupervised algorithms .Major classification method are decision tree induction, Bayesian networks, linear programming, neural network and fuzzy logic technique. [13][26][27][28][29][30].

2. CLUSTERING: It is important DM Technique; Clustering groups similar and dissimilar objects. There are number of clustering models which can be used for different applications. It can also be used as a pre-processing approach for attribute subset selection and classification [4].Clustering mainly used for pattern recognition, machine learning and information retrieval.

2.1 K-MEANS CLUSTERING: It is very Simple clustering approach, less complex method and also efficient. In advance it requires number of cluster to proceed further. It is having problem in handling categorical attributes. It will not predict the cluster with non-convex shape. Outcome varies in the presence of outlier.

2.2 HIERARCHICAL CLUSTERING: Easy to implement and having good visualization capability. Not necessary to mention the number of clusters in advance. It has cubic time complexity in many cases so it is slower. Once a decision is made it cannot be withdrawn. It will not work proper in the presence of noise. It is not scalable one.

2.3 DENSITY BASED CLUSTERING: No need to specify number of cluster in advance. It is very simple to handle cluster with arbitrary shape. It will work well in the presence of noise. It will not handle the data points with varying densities. Results will be based on the distance measure.

3. PREDICTION: It is important DM Technique; The prediction as it name implied is one of a data mining techniques that discovers relationship between independent variables and relationship between dependent and independent variables. Unfortunately many real world problems are not simply prediction. For instance, stock price, sales volumes are difficult to predict, therefore we need more complex techniques like logistic regression, decision tress and neural networks [4].

4. ASSOCIATION: It is important DM Technique; Association is one of the best known data mining technique. In association, a pattern is discovered based on a relationship of a particular item on other items in the same transaction. For example, the association technique is used in heart disease prediction as it tell us the relationship of different attributes used for analysis and sort out the patient with all the risk factor which are required for prediction of disease[4].

5. NEURAL NETWORK: It is important DM Technique; Neural networks models have been studied for many years in the hope of achieving human like performance in several fields. As the human brain consists of millions of neurons that are interconnected by synapses, a neural network is a set of connected input/output units in which each connection has a weight associated with it. The network learns in the learning phase by adjusting the weights so as to be able to predict the correct class label of the input. They are the best at identifying patterns or trends in data and well suited for prediction or forecasting needs .due to their performance neural networks have been widely used in experiments and adopted for critical biomedical classification and clustering problems. Its merits and demerits are it will easily identify complex relationships between dependent and independent variables. Able to handle noisy data, Local minima and over-fitting. The processing of ANN network is difficult to interpret and require high processing time if there are large neural networks[12].

6. DECISION TREE(DT): It is important DM Technique; Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item’s target value. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data but not decisions; rather the resulting classification tree can be an input for decision making. The goal is to create a model that predicts the value of a target variable based on several input variables.

Its merits and demerits are there are no necessities of domain knowledge in the construction of decision tree. It minimizes the ambiguity of complicated decisions and assigns exact values to outcomes of various actions. It can easily process the data with high dimension. It is easy to interpret. Decision tree also handles both numerical and categorical data. It is restricted to one output attribute. It generates categorical output. It is an unstable classifier i.e. performance of classifier is depend upon the type of dataset. If the type of dataset is numeric than it generates a complex decision tree[9].

7. SUPPORT VECTOR MACHINE (SVM): It is important DM Technique; Support vector machine is an algorithm that attempts to find a linear separator (hyperplane) between the data points of two classes in multidimensional space. SVMs are well suited to dealing with interactions among features and redundant features. Its merits and demerits are Better accuracy as compare to other classifier. Over fitting problem is not as much as other methods. Easily handle complex nonlinear data points. It is computationally expensive. The main problem is the selection of right kernel function. For every dataset different kernel function shows different results. As compare to other methods training process take more time. SVM was designed to solve the problem of binary class. It solves the problem of multi class by breaking it into pair of two classes such as one- againstone and one-against- all [9].

8. GENETIC ALGORITHMS (GAS) AND EVOLUTIONARY PROGRAMMING (EP): It is important DM Technique; Genetic algorithms and evolutionary programming are algorithmic optimization strategies that are inspired by the principles observed in natural evolution. Of a collection of potential problem solutions that compete with each other, the best solutions are selected and combined with each other. In doing so, one expects that the overall goodness of the solution set will become better and better, similar to the process of evolution of a population of organisms. Genetic algorithms and evolutionary programming are used in data mining to formulate hypotheses about dependencies between variables, in the form of association rules or some other internal formalism [12].

9. FUZZY SETS (FS): It is important DM Technique; Fuzzy sets form a key methodology for representing and processing uncertainty. Uncertainty arises in many forms in today’s databases: imprecision, non-specificity, inconsistency, vagueness, etc. Fuzzy sets exploit uncertainty in an attempt to make system complexity manageable. As such, fuzzy sets constitute a powerful approach to deal not only with incomplete, noisy or imprecise data, but may also be helpful in developing uncertain models of the data that provide smarter and smoother performance than traditional systems[12].

10. ROUGH SETS(RS): It is important DM Technique; in this a rough set is determined by a lower and upper bound of a set. Every member of the lower bound is a certain member of the set. Every nonmember of the upper bound is a certain non-member of the set. The upper bound of a rough set is the union between the lower bound and the so-called boundary region.

11. K-NEAREST NEIGHBOR (KNN): It is important DM Technique. It is very easy to implement. Training is done in faster manner. It requires large storage space. It is very sensitive to noise. Testing is slow [14].

12. BAYESIAN BELIEF NETWORK (BBN) It is important DM Technique, It makes computations process easier. Have better speed and accuracy for huge datasets. It does not give accurate results in some cases where there exists dependency among variables [3].

III. DATA MINING MERITS AND DEMERITS MERITS:

• It predicts future trends

• It helps in decision making

It helps to improve company revenue

• It is mainly used in Market Analysis

• It is effectively utilized in fraud detection

• DM techniques applied in health care insurers to detect fraud and abuse.

• DM helps the Physicians to identify effective treatments and best practices through healthcare software’s.

• Using data mining it is possible to speed up the work in large data sets.

• DM facilitates generation of quicker reports and faster analysis, which will increase operational efficiency and also diminishes operating cost.

• Data mining can extract predictive information from large database, which is a very important.

DEMERITS:

• Heterogeneity of data volume and complexity will create unnecessary mathematical categorization.

• Must consider Ethical legal and Social issues.

• Dealing Data ownership, Lawsuits

• Privacy and Security of Human Data Administrative Issues – Medical data.

• Other general Security Issues.

• Misuse of information or in accurate information.

V. DATA MINING TECHNIQUES APPLIED DOMAINS: Data mining is an interdisciplinary field and with wide diverse applications. There be nontrivial gaps between data mining principles and domain-specific applications, few application domains of Data Mining are listed below, • Healthcare • Finance • Retail industry • Telecommunication • Text Mining • Web Mining • Higher Education , etc Tremendous results and reports received in all the above fields by the effective utilization of DM[30].

VI. CONCLUSION: In this paper we presented various data mining techniques that have been employed for medical data mining. Data mining techniques have higher utility in medical data mining as there is voluminous data in this industry. Due to the rapid growth of medical data, it has become in dispensable to use data mining technique to help decision support and prediction system in the field of health care. This paper has provided the summary of data mining techniques used for all the domains.

ACKNOWLEDGEMENT We acknowledge the immense help from scholars whose articles are cited and included in references of this manuscript.

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A study by Raunak Das entitled "Study of Cardiovascular Dysfunctions in Interstitial Lung Diseas epatients by Correlating the Levels of Serum NT PRO BNP and Microalbuminuria (Biomarkers of Cardiovascular Dysfunction) with Echocardiographic, Bronchoscopic and HighResolution Computed Tomography Findings of These ILD Patients" is awarded Best Article of Vol 12 issue 13

A Study by Kannamani Ramasamy et al. entitled "COVID-19 Situation at Chennai City – Forecasting for the Better Pandemic Management" is awarded best article for Vol 12 issue 12

A Study by Muhammet Lutfi SELCUK and Fatma entitled "Distinction of Gray and White Matter for Some Histological Staining Methods in New Zealand Rabbit's Brain" is awarded best article for Vol 12 issue 11

A Study by Anamul Haq et al. entitled "Etiology of Abnormal Uterine Bleeding in Adolescents – Emphasis Upon Polycystic Ovarian Syndrome" is awarded best article for Vol 12 issue 10

A Study by entitled "Estimation of Reference Interval of Serum Progesterone During Three Trimesters of Normal Pregnancy in a Tertiary Care Hospital of Kolkata" is awarded best article for Vol 12 issue 09

A Study by Ilona Gracie De Souza & Pavan Kumar G. entitled "Effect of Releasing Myofascial Chain in Patients with Patellofemoral Pain Syndrome - A Randomized Clinical Trial" is awarded best article for Vol 12 issue 08

A Study by Virendra Atam et. al. entitled "Clinical Profile and Short - Term Mortality Predictors in Acute Stroke with Emphasis on Stress Hyperglycemia and THRIVE Score : An Observational Study" is awarded best article for Vol 12 issue 07

A Study by K. Krupashree et. al. entitled "Protective Effects of Picrorhizakurroa Against Fumonisin B1 Induced Hepatotoxicity in Mice" is awarded best article for issue Vol 10 issue 20

A study by Mithun K.P. et al "Larvicidal Activity of Crude Solanum Nigrum Leaf and Berries Extract Against Dengue Vector-Aedesaegypti" is awarded Best Article for Vol 10 issue 14 of IJCRR

A study by Asha Menon "Women in Child Care and Early Education: Truly Nontraditional Work" is awarded Best Article for Vol 10 issue 13

A study by Deep J. M. "Prevalence of Molar-Incisor Hypomineralization in 7-13 Years Old Children of Biratnagar, Nepal: A Cross Sectional Study" is awarded Best Article for Vol 10 issue 11 of IJCRR

A review by Chitra et al to analyse relation between Obesity and Type 2 diabetes is awarded 'Best Article' for Vol 10 issue 10 by IJCRR.

A study by Karanpreet et al "Pregnancy Induced Hypertension: A Study on Its Multisystem Involvement" is given Best Paper Award for Vol 10 issue 09

List of Awardees

A Study by Ese Anibor et al. "Evaluation of Temporomandibular Joint Disorders Among Delta State University Students in Abraka, Nigeria" from Vol 13 issue 16 received Emerging Researcher Award

A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" from Vol 13 issue 06 received Emerging Researcher Award

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IJCRR

A STUDY ON DATA MINING TECHNIQUES, METHODS, TOOLS AND APPLICATIONS IN VARIOUS INDUSTRIES

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