Facts on Data Mining

Facts on Data Mining

Data mining is the process of examining a data set to extract certain patterns. Companies use this process to determine the outcome of their existing goals. They summarize this information into useful methods to create revenue and/or cut costs. When search engines are accessed, they begin to build lists of links from the first page it accesses. It continues this process throughout the site until it reaches the root page. This data not only includes text, but also numbers and facts.

Data mining focuses on consumers in relation to both "internal" (price, product positioning), and "external" (competition, demographics) factors which help determine consumer price, customer satisfaction, and corporate profits. It also provides a link between separate transactions and analytical systems. Four types of relationships are sought with data mining:

o Classes - information used to increase traffic
o Clusters - grouped to determine consumer preferences or logical relationships
o Associations - used to group products normally bought together (i.e., bacon, eggs; milk, bread)
o Patterns - used to anticipate behavior trends

This process provides numerous benefits to businesses, governments, society, and especially individuals as a whole. It starts with a cleaning process which removes errors and ensures consistency. Algorithms are then used to "mine" the data to establish patterns.

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Resume Extraction: To Grab Best Candidate

Resume Extraction: To Grab Best Candidate

Selecting the eligible and potential employee for the organization is the most significant task of any company. Success rate of any company totally depends on the assortment of talented and experienced candidates. Quality is of prime significance than quantity and for this, having the best resume analyzer is a good idea. The tasks related to recruitment should be performed well by the HR department.

Examination of a perfectly apt candidate is the main concern of the qualitative resume software. A number of myriad aspects are considered for the resume assessment. There posses a competition of various talents that candidate possesses. Before recruitment of any applicant, his job analysis is performed by the HR department. For this purpose performing resume extraction becomes essential and resume analyzer is the medium to do so.

Proficient software performs a helpful task at job portals. The resume analyzer parses all the resumes and filters them on the basis of presence of keyword. It facilitates to match the particular keyword with every available resume. Presence of keywords indicates that the candidate is short listed while absence refers rejection. As these days everyone needs fast results performing resume extraction becomes essential to save time and money.

Resume analyzer helps in accepting and rejecting the resume of the candidates. It position or rank the candidates in to a list, this criteria is based on the presence of the keywords and the required apt information about the candidate. Resume software implements the standard policies for formatting the process of resume extraction and uploads this important data into your available database. This data is available in the text format. Essential information like name, qualifications, contact details, certifications, last work experience etc present in resume is uploaded into the database.

This information is used to match the criteria of the required job post. Ranking of the candidates helps to opt for the most suitable and skilled candidate among the list of thousands.

Resume extraction is one of the essential aspects to sort out the potential candidate.

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Using Charts For Effective Data Mining

Using Charts For Effective Data Mining

The modern world is one where data is gathered voraciously. Modern computers with all their advanced hardware and software are bringing all of this data to our fingertips. In fact one survey says that the amount of data gathered is doubled every year. That is quite some data to understand and analyze. And this means a lot of time, effort and money. That is where advancements in the field of Data Mining have proven to be so useful.

Data mining is basically a process of identifying underlying patters and relationships among sets of data that are not apparent at first glance. It is a method by which large and unorganized amounts of data are analyzed to find underlying connections which might give the analyzer useful insight into the data being analyzed.

It's uses are varied. In marketing it can be used to reach a product to a particular customer. For example, suppose a supermarket while mining through their records notices customers preferring to buy a particular brand of a particular product. The supermarket can then promote that product even further by giving discounts, promotional offers etc. related to that product. A medical researcher analyzing D.N.A strands can and will have to use data mining to find relationships existing among the strands. Apart from bio-informatics, data mining has found applications in several other fields like genetics, pure medicine, engineering, even education.

The Internet is also a domain where mining is used extensively. The world wide web is a minefield of information. This information needs to be sorted, grouped and analyzed. Data Mining is used extensively here. For example one of the most important aspects of the net is search. Everyday several million people search for information over the world wide web. If each search query is to be stored then extensively large amounts of data will be generated. Mining can then be used to analyze all of this data and help return better and more direct search results which lead to better usability of the Internet.

Data mining requires advanced techniques to implement. Statistical models, mathematical algorithms or the more modern machine learning methods may be used to sift through tons and tons of data in order to make sense of it all.

Foremost among these is the method of charting. Here data is plotted in the form of charts and graphs. Data visualization, as it is often referred to is a tried and tested technique of data mining. If visually depicted, data easily reveals relationships that would otherwise be hidden. Bar charts, pie charts, line charts, scatter plots, bubble charts etc. provide simple, easy techniques for data mining.

Thus a clear simple truth emerges. In today's world of heavy load data, mining it is necessary. And charts and graphs are one of the surest methods of doing this. And if current trends are anything to go by the importance of data mining cannot be undermined in any way in the near future.

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