Oftentimes, to properly arrange data seems unattainable because the necessary data is endlessly coming in while the older files are being set aside and piled up. Unless someone or a group of individuals are held responsible for putting the right stuff in the right places, there will eventually be an overwhelming overload of disorganized database.

It can be reassuring to know that the art of data mining does the systematic arrangement of information as it directs you to the right sources and gives you the needed data for your business or research.

Whatever type of data mining you choose, be it supervised data mining or unsupervised, there is always a systematic putting of things in their proper places for easy reference and access. Being organized can now be attainable and it surely comes as a twin of data mining.

Supervised data mining

The supervised data mining includes classification; regression; and attribute importance. Obviously, these are ways of putting the right stuff in the right places where they belong and thus aid in the management of databases.

Classification. Putting similar data into the same categories or classes is the mechanics of classification. In data mining, it can be done according to a model based on a historical data where new data can easily be fit into the categories previously created and identified. Moreover, as classes of data are formed and recognized, it would be easier to predict the class of each new data. This is very helpful in the analysis of trends and inflows of new content.

Regression. Like classification, regression uses models too. However, the focus is more on numerical or the so-called continuous target in contrast to classification’s discrete or categorical features. The key word here is continuous.

Attribute Importance. As an aid to classification, Attribute Importance (AI) renders an automatic answer to improve the speed and even the accuracy of classification models which are built on data tables that have great number of attributes.

Unsupervised data mining

On the other hand, unsupervised data mining includes clustering; association; feature extraction; and anomaly detection (one-class classification). Like supervised data mining, the gathered information is arranged using different methods according to the type and use of the data collected.

Clustering. This method is vital in the exploration of data. This is usually done when there are varied data but are without clear groupings. In this way, natural groupings can be found.

Association. This model is usually used in market basket analysis. It is done by trying to see the relationships and any correlations between a set of items. Market basket analysis is often applied in direct marketing, and catalog design as well as in other businesses particularly in decision-making processes.

Feature Extraction. In feature extraction, a set of structures is created based on the previous original data. A feature is a mixture of qualities that are specially found in the data thus it becomes is of special interest and captures important characteristics of the data. It becomes a new attribute. Actually, there are usually lesser features than original attributes.

Anomaly Detection. Anomaly detection entails the identification of new or irregular patterns. This is especially useful in problems relating to fraud such as in insurance, tax and credit card issues. This is also useful in detecting computer network intrusion

As a layman, the above data mining language and terms may sound foreign or may somewhat be confusing. The point here is that  organizing data is so much a part of data mining that it solves so much of the mess that data acquisition may cause. There is no need of hiring additional personnel to do the arranging and segregating of vital information. At this juncture, there is also no need for extra expenses. The cost of data mining itself covers a whole lot of benefits that it can indeed be considered truly cost effective.

Just imagine a virtual storage of important information and data that you do not have to worry about with regards to its classification, identification, importance and readiness in time of need. This is exactly what data mining will do for you and much more to your delight and surprise. However, there is always that caution that you have to adhere to every time you go data mining or hire a data mining service provider. You have to know the limits, risks and responsibilities in performing it. Be sure to acknowledge the source, ask permission to use copyrighted and original materials, and do not ever plagiarize.

There is now a very thin line between original and plagiarized material. Because of the rise of income generating activities online, some people, out of sheer ignorance or laziness have crossed the line and committed the worst of all crimes in writing. It is very alarming that some persons have become victims of careless and lazy internet users.

Sadly, web scraping can be abused and become an avenue for plagiarism. If individuals are not careful, the beauty and blessing of this new innovation in business and research may turn into an ugly curse.

Benefits of web scraping

At present, web scraping or data mining has become a popular way of doing research online. Truly, there are numberless benefits of web scraping and it has been the reason why many businesses, companies and studies have become successful over a relatively shorter time. Never in the history of mankind has individuals been able to earn much money within the confines of their homes as is the case these days. Moreover, now is the time when predicting future trends in business has been made more realistic and attainable. Indeed web scraping has done wonders to the human life than anyone could have imagined before.

Risks of web scraping

In view of the aforementioned benefits, there are also some risks of web scraping that need to be taken careful consideration of. The issue of plagiarism has now become the focus of some serious and dedicated writers. Inevitably, there is now rampant copying and careless rewriting that is committed by certain persons and websites. Consequently, browsing the net would sometimes become frustrating because many articles that are contained in some sites are poorly written and difficult to understand because these are copied from original sources and some words are simply changed and content is rearranged to escape tests on plagiarism. Careless rewriting and spinning have mushroomed in the past few years because of the demand for SEO by many websites.

Caution in web scraping

One should always exercise caution in web scraping, since any content taken from others even if you rephrase them can be considered plagiarism without acknowledging the source or seeking permission to use such materials. It must be noted that there are other factors more important than money with regards to web research and data mining. Having received no complaints or not getting caught are not valid excuses for plagiarism. Web scraping entails responsible and reliable acts because the authors have painstakingly created their materials spending so much time and money in order to come up with factual and original works. It would surely be unfair to glean from their works and get the same benefits from it.

Ethical concerns

Web scraping is indeed beneficial and worth its value but there are more important issues to tackle before fully indulging in it and enjoying its limitless benefits.

Maintaining personal integrity. Having a clear conscience cannot be bought. It is as fleeting as the waves of the sea. Every now and then you are tempted to cheat because it’s the easiest way to get things done. The famous expression “copy and paste” is not only convenient, but it also seems to be legal since many people are doing it. However, please remember that not everything that everybody else does is always correct. Copy and paste is stealing in the simplest and truest sense.

Working honestly. To silently work your way up the ladder of success without any form of deception is the best reward one can ever have. Knowing that you do not step on another’s foot and have acknowledged every single detail of everyone else’s help can make you go miles and miles without fear. Even if it may sound old fashioned, honesty is still the best policy. You do your tasks honestly and you will gain more than just financial profits but friends and allies too.

Acknowledging the source. The easiest way to stay out of plagiarism’s zone is to acknowledge the source. It is like entering a neighbor’s domain by the front door and ringing the doorbell.

Seeking permission to repost or cite. The best way to avoid plagiarism though is asking permission from the authors to quote, cite or repost their ideas and materials. With this, you can be assured that you are not stealing anything and that you are on safe ground. Who knows the relationship you may establish with them may grow much deeper into a constant partnership. You do not only get new insights but you can also be more proud of yourself.

Web scraping should indeed entail both a privilege and a responsibility; benefits and accountability; as well as respect and good conscience.

I hope you have invested a lot of resources and money to a good idea and now it is time to see fruits of your labor ripening. If you believe you have not invested a lot of your resources, it is time you consider yourself a resource! Nowadays every business is living on information. You have the liberty to think of a business that does not require information. What creates the difference is ability of collecting this information in an effective and efficient manner. A question arises; what is the right time to gather the information? Whether you just have an idea or running a successful enterprise, there is wrong time but probably the right time is now. Anytime you gather information you are gaining competitive advantage over your competitors. ]]> dheerajjuneja@gmail.com (Abel Nyarangi) Further Reading Sun, 29 Apr 2012 17:48:23 +0000 http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/219-the-role-of-association-rules-in-data-mining http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/219-the-role-of-association-rules-in-data-mining An interesting part of the data mining phenomenon is its being anchored on association rules. This paradoxical relationship between things is shown in the “if/then statements.” The “if” statement is called the antecedent while the “then” statement is the consequence. It is considered a paradox because two things or items can be found to be paired many times although they are so much different from each other and may not have any connections at all.

This rule aids the researcher or business person to identify the relationships between seemingly unrelated data in the database. The most common example studies reveal is: If a person buys eggs, he is most likely going to buy milk too.

How it came about

According to some stories, as early as 1992, a team in a retail consulting group made a study of numerous market baskets and found out that beer and diaper are bought together by young men. Although it may appear so far apart; that is, how can beer and diaper be connected? It may sound absurd that beer-drinking men would be using the diapers because obviously the diapers mentioned here are to be used by babies. It would equally be absurd if one may say that the babies drink the beer and so they would urinate more frequently. This association is revealed through analyzing the relationships between two unrelated items.

How it works

As stated above, an association rule is made up of two parts namely: an antecedent (if); and a consequent (then). The antecedent is an item that is found in the information bank. In addition, the consequent is another item which is found to be combined with the antecedent.

The association rules are formed by studying data with recurrent “if/then” configurations long with the criteria “support and confidence.” This is done in order to find the most significant connections. Support stands for the frequency of the appearance of the item in the database; while confidence refers to the number of occurrences that the “if/then” statements are true.

Association rules are important in data mining particularly in analyzing and predicting consumer behavior. They are significant in product grouping, shopping basket data analysis, store layout and catalog design.

What it entails

There are several data mining algorithms that are involved in generating association rules such as: Apriori, Eclat and FP-Growth. However, these are mainly doing half of the task because they are algorithms for mining recurrent mining sets; the rest is done for the frequent itemsets in the database.

Apriori is the most well-known algorithm for mining association rules. It is more of a breadth-first search strategy in order to calculate the support of itemsets. In addition, it makes use of a candidate generation function that exhausts the descending closure property of support. Eclat algorithm, on the other hand is depth-first search algorithm that uses set intersection.

Further, FP-growth or frequent pattern growth makes use of an extended prefix-tree (FP-tree) structure to stock the database in a compacted form. Another algorithm is the GUHA which is a general way for exploratory data analysis that owes its beginnings in observational calculi.  Lastly, the OPUS search is an efficient algorithm to generate association rule. The OPUS search is the main technology in the famous Magnum Opus association discovery system.

What more can it do

Aside from the grocery basket analysis, the association rules can also be used in contrast set learning which a system of associative learning is. Contrast set learners makes use of rules that are meaningfully differ in their dissemination through subsets. Other related fields are: weighted class learning; K-optimal pattern discovery; mining frequent sequences; generalized association rules; quantitative association rules; interval data association rules; maximum association rules; and sequential association rules.

Conclusion

After all that has been said about the benefits and the grandeur of data mining, it is amazing to know that the more it is used the more it gives out more positive reaults. It is a dyanmic aspect of the present-day innovations and more surprises. 

It is an interesting awareness that association rules play a vital role in data mining. Through this, what appears to be unrelated can have a logical explanation through careful analysis and after some time. This aspect of data mining can be very useful in predicting patterns and foreseeing trends in consumer behavior, choices and preferences. Association rules are indeed one of the best ways to succeed in business and enjoy the harvest from data mining.

The technology is used to enable clients, companies to obtain, generate and use the large quantities of data. Usually companies rely greatly on data mining for a number of reasons such as marketing, database providers, manufacturing, travel industry, financial and banking services, engineering, and telecommunications among others.

The common idea about data mining is that all the industries have at their disposal enormous amounts of information. The information can be about their clients or even operations and harvested in different ways. So as to maximize on the usefulness of the data, they have incorporated different techniques that drive services such as web scraping, data extraction, web data mining that help them to glean particular trends and patterns from the data and also offering simulations and predictions concerning the future events.

For instance, it is no surprise for a biopharmaceutical industry to regularly employ a number of data mining methodologies that enable it to deal with large amounts of biological data in different forms that have been collected by the industry. From the annotated databases of molecular pathways and disease profiles to structure, sequences, population and individual clinic tests, this industry has inundated with information at its disposal, and data mining becomes the core of advanced methodologies that deal with information overload.

The technology

So let’s understand this technology, data mining. It uses a technology known as machine learning and visualization and statistical methodologies in representing and discovering knowledge which is easily understood by humans. The main idea here is to extract, reduce complexity, or mine, useful and relevant information from databases. ]]> dheerajjuneja@gmail.com (Abel Nyarangi) Further Reading Wed, 18 Apr 2012 16:06:08 +0000 http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/217-data-mining-and-its-importance- http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/217-data-mining-and-its-importance- We can simply define data mining as a process that involves searching, collecting, filtering and analyzing the data. It is important to understand that this is not the standard or accepted definition. But the above definition caters for the whole process.

Large amount of data can be retrieved from various websites and databases. It can be retrieved in form of data relationships, co-relations and patterns. With the advent of computers, internet and large databases it is possible collect large amounts of data. The data collected may be analyzed steadily and help identify relationships and find solutions to the existing problems.

Governments, private companies, large organizations and all businesses are after large volume of data collection for the purposes of business and research development. The data collected can be stored for future use. Storage of information is quite important whenever it is required. It is important to note that it may take long time for finding and searching for information from websites, databases and other internet sources.

Data mining services can be used for the following functions

• Research and surveys. Data mining can be used for product research, surveys, market research and analysis. Information can be gathered that is quite useful in driving new marketing campaigns and promotions.
• Information collection. Through the web scraping process it is possible to collect information regarding investors, investments and funds by scraping through related websites and databases. ]]> rakquel105@yahoo.com.ph (Abel Nyarangi) Further Reading Fri, 13 Apr 2012 09:33:54 +0000 http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/216-why-outsourcing-data-mining-services-is-the-leading-business-trend http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/216-why-outsourcing-data-mining-services-is-the-leading-business-trend Businesses usually have huge volumes of raw data that remains unprocessed. Processing data results into information. A company’s hunt for valuable information ends when it outsources its data mining process to reputable and professional data mining companies. In this way a company is able to derive more clarity and accuracy in the decision making process.

  It is important too note that information is critical to the growth of a business. With the internet you are offered flexible communication and good flow of data. It is a good idea to make the data that is available readily and in a workable format where it will be useful to a business. The filtered data is deemed important to the organization and the services can be used to increase profits, ameliorating overall risks and smooth work flow.

  Data mining process must engage the sorting data process through the vast data amounts of data and acquire pertinent information. Data mining is usually undertaken by professional, financial and business analysts. Nowadays, there are many growing fields that require data extraction services.

  When making decisions data mining plays an important role as it enables experts to make decisions quick and in a feasible manner. The information that is processed finds wide applications for decision making that relate to e-commerce, direct marketing, health care, telecommunications, customer relationship management, financial utilities and services.

  The following are the data mining services that are commonly outsourced to the professional data mining companies:

  1. Data congregation. This is the process of extracting data from different websites and web pages. The common processes involved here include web scraping and screen scraping services. The data congregated is then in put into databases. ]]> dheerajjuneja@gmail.com (Web Mining Team) Further Reading Tue, 03 Apr 2012 22:17:32 +0000 http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/215-things-you-should-know-about-data-mining- http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/215-things-you-should-know-about-data-mining- If you think data mining is just one of those emerging and fleeting innovations of the 2ist century, then you can stop and read this whole article. Like a magical deep well, this development can open ways to a lot of possibilities and surprises never ever been imagined before.

     

     Data analysis and trends

     Data analysis is a unique feature of data mining along with a host of other seemingly impossible things it can contribute to the betterment of human life. It must be understood that data mining is not limited to online data collection alone; it also includes, but is not limited to, the statistical analysis and techniques that are used to search over huge amounts of data in order to determine trends or patterns. This method of inquiry and system of acquiring knowledge is beneficial to all fields of human endeavor particularly in business and research.

     Data mining as by-product of internet

     Data mining is a fast and easy way of employing an exclusively and potent tool in the data analysis and evaluation of vast databases. Since the internet has been in full bloom, so much data has become available online. So much data can be procured from one scientific research for instance such that visual inspection can no longer be enough to come up with a valid and reliable explanation of the information. This gives rise to computer-generated solutions like the data mining. It was in the 1990s when natural science and computer science have become interrelated in order to yield objective and intelligent interpretations.

     Giving solutions to timeless problems

     Numerous problems have already been solved and helped by data mining. Its ability in predicting trends is one of its best influences in urging nations and institutions to utilize it for their own benefits.  For example, government and organizational activities and profile have been benefited by data mining, such as in storing, collecting and monitoring of information in the said fields. With data mining, undesirable and irrelevant information can be detected and eradicated. This is specially benefiting criminal investigations and identifications of fraud. From tracing the patterns of activities of suspects as well as their locations and contacts, catching the culprit is made quicker and accurate.

     Formulas called algorithms

     Data mining uses formulas called algorithms. The two most common data mining algorithms are called classification analysis and regression analysis. Classification analysis is used to analyze data that is not numerical or qualitative data such as colors, names or opinions. This is also called the descriptive model. On the other hand, numerical or quantitative data makes use of regression analysis. A mathematical formula is constructed to describe the pattern of the data which will then be able to predict the future performance of the data, thus it is also called the predictive model.

     Steps in data mining process

     The process of data mining has at least seven steps, namely: definition of the problem; building of the database; examination of the data; preparation of the model to be used to examine the data; testing of the model; use of the model; and putting of the results to action. These may sound complicated but with proper understanding of the data mining mechanics and techniques, you can get the best benefits you have never experienced before.

     Linking of data from different branches

     With data mining, it is now easier to connect with other units, agencies and branches to get the information needed. For example, the FBI and the CIA may have different databases but these two agencies can link together in order to acquire the needed results such as in pursuing a criminal or in identifying fraud. The only problem with linkages is the differing structure and formats of their databases. It is therefore necessary to coordinate with each other and make use of similar of the same templates.

     This dynamic concept of data mining has indeed become so broad and yet so specific. Understanding all these may appear overwhelming at first especially that it makes use of unfamiliar terms processes. However, time spent in learning its mechanisms and applying its processes cannot be compared to the enormous benefits it will bring you. Whatever your field of expertise is, whether it is business, research, social causes, nonprofit civic organizations, and others, you can greatly benefit from the blessings of data mining.

     If there is any negative issue about data mining, it can be about the usual problems of data collection and storage such as plagiarism, intellectual property rights, and the like. Such problems have already been dealt with by the government, so the same solutions can be applied with the use of data mining. It is therefore necessary for every data miner to be cautious and responsible while enjoying its benefits. There should be a balance in everything; thus, the risks must be faced with ready weapons of accountability and objectivity.

     ]]> rakquel105@yahoo.com.ph (Web Scraping Expert) Further Reading Sun, 01 Apr 2012 10:33:19 +0000 http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/214-data-mining-in-new-dimension http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/214-data-mining-in-new-dimension Data mining in New Dimension

     Data mining is one of the most recent technologies that are currently used in data harvesting and analysis. It is an important process for every business whether large, medium or small-sized. This is because information is a key to any business success. Nowadays, there is an emergence of data mining companies, whose role is to help other business in gathering, collecting and analyzing data.

     Data mining can be defined as techniques that are used in identifying relationships which have been previously not discovered. This process is able to find different patterns found in the data. As mentioned earlier, these patterns have not been discovered earlier and should also not be reliant to the existing database. This process is quite relatively easy and requires the knowledge of the subject or problem that matter expertise.

     Data mining is quite different from other processes such as statistical analysis. It is important to note that data mining was originally developed to act as expert systems in solving problems. This is not the case with statistical analysis as it is intended for statistical correctness of models. Also data mining is less interested in the mechanics of the technique itself while the latter is quite interested in the mechanics. ]]> dheerajjuneja@gmail.com (Web Mining Team) Further Reading Tue, 27 Mar 2012 05:32:56 +0000 http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/213-tips-for-data-mining-success http://www.loginworks.com/web-scraping-blogs/35-web-scraping-further-reading/213-tips-for-data-mining-success You may have tried data mining before but you seem to be lost in the maze of confusion, data overload, and a number of strange terms and icons. Do not fret, you are not alone. There may be a number of first timers who are in the same boat as you do. Stop, refocus and start all over again with the following tips in mind.

     It is important that proper handling of the data mining procedure must be employed. Easy as it may sound, it can only bring in great results when it is placed in the expert hands and when done according to the right patterns and processes. This is not to say that data mining is only successful for a gifted and trained few. It means serious consideration, preparation, and training must be part of the groundwork before disembarking into it.

     The most practical and tested tips are: know your desired outcomes; set expectations; assign the right personnel; avoid data dump; create a deployment scheme; develop a maintenance plan.

     Know your desired outcomes

     As the major proprietor of your business, you of all people should have a clear view in mind of what you really want for your business. Thus, before trying on new strategies and techniques that are recommended to you, you must know what your desired outcomes are. For instance, if your business is in real estate, you must be able to foresee which direction your market should go. Are you going up on skyscrapers or towards the horizons in the countryside? From great lengths, you go to the specifics and clearly spell out what you want and where it should be.

     Set expectations

     In connection with identifying your outcomes, you must also set realistic and attainable expectations. These are the very things that preclude possible obstacles and frustrations in the coming years. You can see where your business is going by web research or data mining. You can see the past and present of your competitors and you can also set your own future based on the experiences of others. It is often wise to set expectations that you have not attained before. It is like plowing and preparing the ground because you know rain is coming and it is the right time to plant and gain great harvest.

     Assign the right personnel

     When you find the right person as well as the right data mining service, you can cut short tiresome planning, devising and preparation. If you are in a small enterprise, you can spearhead the procedure but if you have enough staff at your disposal, choose one who is not only knowledgeable but also reliable and dedicated. You do not want someone who is only a good starter and one who would leave you hanging when the going gets tough.

     Avoid data dump

     Being sure of what you want can help you avoid unnecessary data. Data mining like real mining is being able to know where the gold is and is able to get it done in the most efficient and effective way. Being able to identify the legal sites and reliable, well researched information is the short cut to finding the right and exact data. It would be a waste of time and effort if you are aimlessly opening and clicking on unsure and ambiguous websites. There are a lot of links that lead you to more links and are simply making money out of others’ ignorance.

     Create a deployment scheme

     Like any other venture, you must also be able to delegate the task as well as the information that you gather. Since you are not a superhuman, learn to seek the assistance of others and be sure that you know who to trust. In addition, you must have a classification and segregation of the needed materials so that these will be easy to locate and analyze. In other words, order and proper organization is another tip in order to achieve success in data mining.

     Develop a maintenance plan

     Finally, along with orderliness and efficiency, you must see to it that you have an effective maintenance plan. What to do with old data and where to store the vital ones are concerns that need to be considered too. In addition, there is a need for a watchdog in the whole duration of your business venture. This will not only assure you of security of your data but also keep you on healthy and solid ground. This maintenance can be both a cleaning and healing spot for your business’ overall life and sustainability.

     So much can be said about how to go about with your business using data mining but there is a factor that is uniquely your own. Above and beyond all these techniques and strategies, trust your instincts. You are the better judge of your desires and actions; thus, you must spend time alone in reflection, contemplation and retrospection. Being silent and alone can make you see things that are missed among all the movements and noise. Once in a while, leave the scene and look objectively at your work. Remember, there is wisdom in alienation and objectivity.

      

     ]]> rakquel105@yahoo.com.ph (Web Scraping Expert) Further Reading Fri, 23 Mar 2012 15:39:31 +0000