scholarly journals Novel Method for Cricket Match Outcome Prediction using Data Mining Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 15-21
Keyword(s):  
Data Mining ◽  
Outcome Prediction ◽  
Data Models ◽  
The Other ◽  
Data Mining Techniques ◽  
Key Players ◽  
The Future ◽  
Novel Method ◽  
Bayes Algorithm ◽  
Using Data

Cricket is one of the most popular games in many countries. As many as 19 countries play cricket as their main game, and the number is likely to increase in the future. However, there are no suitable tools for analyzing pre-outcome of the match from beginning to end. Existing tools do not support simulating match using batting partnerships. The ultimate goal of predicting pre-outcome of a cricket match is to identify key players and their batting performances. It is also to prevent wrong players from selecting and toss decision by making statistical predictions. This research focuses on One Day International (ODI) cricket match and predicts the outcome of a particular match. Our solution consists of three major modules, namely, Web UI Module, CRIC-Win Analytic Engine, and Backend Data Module. CRIC-Win Analytic Engine has two sub data models, one for predicting the overall match outcome based on a given pre-match data, and the other for predicting match outcome based on batting partnership of both home and rival teams. All sub-models in the CRIC-Win Analytic Engine are developed using the Naïve Bayes algorithm for generating the classifier model, which is used to predict the outcome of the cricket match.

A Study of XML Models for Data Mining

Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

A Study of XML Models for Data Mining

2011 ◽  
pp. 1-28
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

PENERAPAN DATA MINING MENGGUNAKAN METODE TEKNIK CLASSIFICATION UNTUK MELIHAT POTENSI KEPATUHAN WAJIB PAJAK BUMI DAN BANGUNAN

2019 ◽  
Vol 20 (2) ◽  
pp. 157-168
Author(s):  
Qoriani Widayati
Keyword(s):  
Data Mining ◽  
Regional Development ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Regional Government ◽  
Data Mining Techniques ◽  
Know How ◽  
Bayes Algorithm ◽  
Using Data

The goverment implements development in Indonesia, requires substantial funds. The entry of cash from the Land and Building Tax is the most important part for the development of a region, with the results that have been obtained by the regional government can increase regional development with various infrastructures that help the community to carry out various activities and make the area more advanced. One type of tax is the Land and Building Tax (PBB). With the increasing number of taxpayers and data paying contributions directly into the treasury of state finances, the UPT BPPD of SU II Subdistrict of Palembang city did not know how many obedient and non-compliant taxpayers. In this study using data mining techniques, namely classification by applying the Naive Bayes algorithm and getting from the number of taxpayers as many as 1,647 taxpayers with an accuracy of 99.33% which has the potential to not be on time in 16 ulu villages at 0,437 and sub-district households with data of 0.229.

scholarly journals Diagnosis of Diabetes by using Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 2368-2372
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Linear Regression ◽  
Parameter Tuning ◽  
The Other ◽  
Classification Methods ◽  
Data Mining Techniques ◽  
Best Parameter ◽  
Using Data

There are many classifiers that are used for diagnosis of diabetes but the result of this paper shows that how logistic regression having best accuracy among the other classifiers. Logistic regression removes the disadvantages of linear regression. There are different classifiers that are used for prediction. In the worldwide millions of peoples are suffering from diabetes according to WHO report. In the medical region, many researches have done with the help of data mining. The aim of this paper is to diagnosis of diabetes by using the best classifiers and providing best parameter tuning. The study helps to find whether a patient is enduring from diabetes or not using classification methods and it further investigate and evaluates the functioning of different classification in relations of precision, accuracy, recall & roc

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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