Predictive Analysis of Stocks Using Data Mining

2018 ◽  
pp. 283-289
Author(s):  
G. Magesh ◽  
P. Swarnalatha
Keyword(s):  
Data Mining ◽  
Predictive Analysis ◽  
Using Data

scholarly journals Predictive Analysis on Hypertension Treatment using Data Mining Approach in Saudi Arabia

2011 ◽  
Vol 03 (06) ◽  
pp. 252-261 ◽  
Author(s):  
Abdullah A. Aljumah ◽  
Mohammed Gulam Ahamad ◽  
Mohammad Khubeb Siddiqui
Keyword(s):  
Data Mining ◽  
Saudi Arabia ◽  
Predictive Analysis ◽  
Hypertension Treatment ◽  
Data Mining Approach ◽  
Using Data

scholarly journals Predictive Analysis: Assigning Weightage and Difficulty Level of Question using Data Mining

2016 ◽  
Vol 138 (9) ◽  
pp. 31-33
Author(s):  
Kamal Vora ◽  
Sumeet Jain ◽  
Param Mehta ◽  
Smita Sankhe
Keyword(s):  
Data Mining ◽  
Difficulty Level ◽  
Predictive Analysis ◽  
Using Data

Rules Extraction using Data Mining in Historical Data

2016 ◽  
pp. 263-279
Author(s):  
Manish Kumar ◽  
Shashank Srivastava
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Satellite Communications ◽  
Building Blocks ◽  
Predictive Analysis ◽  
Rule Based ◽  
Using Data ◽  
Rules Extraction ◽  
The Impact ◽  
Expected Outcomes

Rules are the smallest building blocks of data mining that produce the evidence for expected outcomes. Many organizations like weather forecasting, production and sales, satellite communications, banks, etc. have adopted this mode of technological understanding not for the enhanced productivity but to attain stability by analyzing past records and preparing a rule-based strategy for the future. Rules may be extracted in different ways depending on the requirements and the dataset from that has to be extracted. This chapter covers various methodologies for extracting such rules. It presents the impact of rule extraction for the predictive analysis in decision making.

scholarly journals Research on Road Traffic Fatal Accidents using Data Mining Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 757-760
Keyword(s):  
Data Mining ◽  
Road Safety ◽  
Prior Information ◽  
Road Traffic ◽  
Clustering Algorithms ◽  
Predictive Analysis ◽  
Apriori Algorithm ◽  
Fatal Accidents ◽  
The Road ◽  
Using Data

Road safety plays a major role in our day-to-day life and also transportation system, due to its priority, it has become the major concern for everyone. In order to increase the road safety, traffic rules are included in education, clear and careful predictive analysis and study is done on factors effecting fatal accidents. We apply predictive analysis, statistical analysis and some algorithms related to data mining which includes FARS such as Apriori algorithm, associative rule techniques are used. These methods help in encountering the road fatal accidents that cause due to mentioned factors. These factors may include climatic and surface conditions and also drunken drivers or may be condition of vehicles also. Clusters are formed using simple k-means clustering algorithms. Finally road safety driving rules are made based on the factors effecting, clusters formed and predictive analysis and prior information.

Rules Extraction using Data Mining in Historical Data

2014 ◽  
pp. 89-106
Author(s):  
Manish Kumar ◽  
Shashank Srivastava
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Satellite Communications ◽  
Building Blocks ◽  
Predictive Analysis ◽  
Rule Based ◽  
Using Data ◽  
Rules Extraction ◽  
The Impact ◽  
Expected Outcomes

Rules are the smallest building blocks of data mining that produce the evidence for expected outcomes. Many organizations like weather forecasting, production and sales, satellite communications, banks, etc. have adopted this mode of technological understanding not for the enhanced productivity but to attain stability by analyzing past records and preparing a rule-based strategy for the future. Rules may be extracted in different ways depending on the requirements and the dataset from that has to be extracted. This chapter covers various methodologies for extracting such rules. It presents the impact of rule extraction for the predictive analysis in decision making.

MONITORING OF OPERATIONAL LOGISTIC PROCESSES IN GENERAL CARGO WAREHOUSES USING PREDICTIVE ANALYSIS

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert
Keyword(s):  
Data Mining ◽  
Human Error ◽  
Predictive Analysis ◽  
Data Mining Algorithm ◽  
It Infrastructure ◽  
Monitoring Procedure ◽  
Manual Recording ◽  
Mining Tool ◽  
General Cargo ◽  
Different Characteristics

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

A Study on Detection of Small Size Malicious Code using Data Mining Method

2019 ◽  
Vol 19 (1) ◽  
pp. 11-17
Author(s):  
Taek-Hyun Lee ◽  
◽  
Ho Kook Kwang
Keyword(s):  
Data Mining ◽  
Malicious Code ◽  
Mining Method ◽  
Data Mining Method ◽  
Using Data

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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