JOURNAL OF FACULTY OF ENGINEERING & TECHNOLOGY, 2015
800x600 In data mining, data cleaning is the most important process for data analysis. Raw data t... more 800x600 In data mining, data cleaning is the most important process for data analysis. Raw data that we collect from different sources may contain noise and missing values that may lead to erroneous results. Hence, data cleansing is required to clarify data from such anomalies. There are different techniques for handling missing values and identifying these outliers. In this paper different statistical techniques for outlier detection are studied and then implemented on weather dataset. A comparison is made at the end of paper to pick the best method. Weather datasets of Lahore, Pakistan was used for applying these methods and collected from online source. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-m...
2014 International Conference on Open Source Systems & Technologies, 2014
Proper maintenance is the key requirement for better performance of any equipment. The physical s... more Proper maintenance is the key requirement for better performance of any equipment. The physical state of equipment affects everything, from production certainty to product quality and customer service levels. Hence, the significance of a well-designed maintenance outage plan is principal. Some companies observe maintenance outages as periodic regular events that interrupt production. Execution proficiency is very crucial to meet the business targets and goals in a timely manner. This paper outlines the unique methods to calculate maintenance outages and then adjust these outages in such a manner that demand supply gap is considered and overall productivity does not suffer.
2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014
Data generation, handling and its processing have emerged as the most reliable source of understa... more Data generation, handling and its processing have emerged as the most reliable source of understanding and discovery of new facts, knowledge and products in the world of natural and material sciences. The emergence of the most efficient techniques in statistical or bioinformatics situations has therefore become a routine practice in research and industrial sectors. Under practical conditions, dealing with large datasets, it's likely to have inconsistencies and anomalies of all kinds to prevent to know real outcomes for practical problems. For accurate data mining computer based techniques of data pre-processing offer solutions that help the data under processing to conform normal structures which in turn considerably improve the performance of machine learning algorithms. In this process, accurate determination of outliers, extreme values and filling up gaps poses formidable challenges. Multiple methodologies have therefore been developed to detect these deviated or inconsistent values called outliers. Different data pre-processing techniques discussed in this paper could offer most suitable solutions for handling missing values and outliers in all kinds of large datasets such as electric load and weather datasets.
JOURNAL OF FACULTY OF ENGINEERING & TECHNOLOGY, 2015
800x600 In data mining, data cleaning is the most important process for data analysis. Raw data t... more 800x600 In data mining, data cleaning is the most important process for data analysis. Raw data that we collect from different sources may contain noise and missing values that may lead to erroneous results. Hence, data cleansing is required to clarify data from such anomalies. There are different techniques for handling missing values and identifying these outliers. In this paper different statistical techniques for outlier detection are studied and then implemented on weather dataset. A comparison is made at the end of paper to pick the best method. Weather datasets of Lahore, Pakistan was used for applying these methods and collected from online source. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-m...
2014 International Conference on Open Source Systems & Technologies, 2014
Proper maintenance is the key requirement for better performance of any equipment. The physical s... more Proper maintenance is the key requirement for better performance of any equipment. The physical state of equipment affects everything, from production certainty to product quality and customer service levels. Hence, the significance of a well-designed maintenance outage plan is principal. Some companies observe maintenance outages as periodic regular events that interrupt production. Execution proficiency is very crucial to meet the business targets and goals in a timely manner. This paper outlines the unique methods to calculate maintenance outages and then adjust these outages in such a manner that demand supply gap is considered and overall productivity does not suffer.
2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014
Data generation, handling and its processing have emerged as the most reliable source of understa... more Data generation, handling and its processing have emerged as the most reliable source of understanding and discovery of new facts, knowledge and products in the world of natural and material sciences. The emergence of the most efficient techniques in statistical or bioinformatics situations has therefore become a routine practice in research and industrial sectors. Under practical conditions, dealing with large datasets, it's likely to have inconsistencies and anomalies of all kinds to prevent to know real outcomes for practical problems. For accurate data mining computer based techniques of data pre-processing offer solutions that help the data under processing to conform normal structures which in turn considerably improve the performance of machine learning algorithms. In this process, accurate determination of outliers, extreme values and filling up gaps poses formidable challenges. Multiple methodologies have therefore been developed to detect these deviated or inconsistent values called outliers. Different data pre-processing techniques discussed in this paper could offer most suitable solutions for handling missing values and outliers in all kinds of large datasets such as electric load and weather datasets.
Uploads
Papers by asma saleem