Data Mining Explained

Overview
Data mining is the crucial process of extracting implicit and possibly useful information from data. It uses analytical and visualization techniques to explore and present information in a format which is easily understandable by humans.

Data mining is widely used in a variety of profiling practices, such as fraud detection, marketing research, surveys and scientific discovery.

In this article I will briefly explain some of the fundamentals and its applications in the real world.

Herein I will not discuss related processes of any sorts, including Data Extraction and Data Structuring.

The Effort
Data Mining has found its application in various fields such as financial institutions, health-care & bio-informatics, business intelligence, social networks data research and many more.

Businesses use it to understand consumer behavior, analyze buying patterns of clients and expand its marketing efforts. Banks and financial institutions use it to detect credit card frauds by recognizing the patterns involved in fake transactions.

The Knack
There is definitely a knack to Data Mining, as there is with any other field of web research activities. That is why it is referred as a craft rather than a science. A craft is the skilled practicing of an occupation.

One point I would like to make here is that data mining solutions offers an analytical perspective into the performance of a company depending on the historical data but one need to consider unknown external events and deceitful activities. On the flip side it is more critical especially for Regulatory bodies to forecast such activities in advance and take necessary measures to prevent such events in future.

In Closing
There are many important niches of Web Data Research that this article has not covered. But I hope that this article will provide you a stage to drill down further into this subject, if you want to do so!

Should you have any queries, please feel free to mail me. I would be pleased to answer each of your queries in detail.



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Outsourcing Data Entry and Data Conversion Services

Data Entry is one such aspects of any business that needs to be handled properly for expanding your business. Online Form Entry Services is one of the leading elements for running a business successfully. Online form entry services are ideally suited to high volume form entry applications such as CD-ROM publication, surveys and questionnaire, database and mailing list compilation, litigation support, electronic publication and multimedia system.

Form Entry plays vital role in every business area. Accumulated data is a powerful management resource. Online entry services cover most business and professional activities, including data conversion, image entry, document and image processing, catalog processing, image enhancement, image editing, and photo manipulation.

In this competitive world, data processing and storage of data in multiple formats is of great essence to every business. It makes the accessibility of data whenever it is needed. Sometimes it becomes headache, but Data conversion services are used to avoid such circumstances.

Data Conversion can be defined as the translation of data from one data format to other format.

Data conversion services include:

o Document conversion
o Book conversion
o CAD conversion
o PDF conversion
o XML/HTML/SGML conversion
o Catalog conversion
o Excel/Word/Email/Page Maker/DBF data format Services.

Affordable and Reliable form entry services provide numeric online entry, textual online entry, image entry, data format and also online data entry, offline data entry with strong quality and time bound. Form entry services also include data processing, image processing, forms processing, document processing, insurance claim processing, data mining and data cleansing services.

so, outsource your data entry work to reliable form entry provider and remove management headaches.



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Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.
It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.
It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

    Extract and load operation data onto the data store system.
    Store and manage the data in a multidimensional database system.
    Provide data access to business analysts and information technology professionals.
    Analyze the data by application software.
    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.



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Data Mining

Data mining is the retrieving of hidden information from data using algorithms. Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. It is basically a technical and mathematical process that involves the use of software and specially designed programs. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data mining is gaining a lot of importance because of its vast applicability. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. It is basically an extension of some statistical methods like regression. However, the use of some advanced technologies makes it a decision making tool as well. Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more.

Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining.

Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms.

Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.



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What Happens When Municipalities Use Rich Data Mining Against Home Businesses to Collect Tax?

Do you will realize how many Americans run a small home business? The number is staggering, and did you know that 10% of our population is self-employed, and that is something like 30 million Americans. That same 30 million Americans, also represents a group that hires over 65% of our population in their small businesses. Folks that started these little firms might expand their business and eventually hire someone, grow their business larger, actually make it into a real company. I'd say that's a good thing, and it shows that the entrepreneurial spirit in the US is alive and well.

Many people don't seem to be aware of these figures or how important they are. Did you know that 10% of our population is self-employed? Don't worry, you're not the only one who hasn't figured this out, even the President of the United States doesn't understand, or obviously he wouldn't have made that political faux pas telling small business people that they didn't build that, or that they couldn't have built their business had it not been for the government providing such a wonderful civilization and society for them to participate in.

Yes, I was a little miffed when he said that as well, because it isn't true, and I've been self-employed my entire life and I've loved my country my entire adult life as well, as have you. Now then, many municipalities are stretched thin with their budgets. Often they owe 60% of all the money they take in, in legacy cost, that is to say pensions, retirement, and health care for people who have already retired from their city employment. That means only 40% of all the money they take collect taxes actually goes to the current city services.

How can any business, much less a government operate on 40% of its income? It can't, and perhaps that's why three cities in California have filed for bankruptcy, along with a couple of other big bankruptcy municipality cases; Birmingham Alabama and Harrisburg Pennsylvania. With city budgets stretched thin they have no choice but to collect more money, and that means finding more ways to tax more people. Most cities require that if you start a business you have to get a business license, and it is considered a tax.

In some cities these taxes are only a $100 or less depending on the type of business you run, but in other cities they can run as much as $500. Most people that start a small business, especially a little home-based business don't bother to register for their business license. They don't make enough money to even afford that when they first start. But guess what? Soon I am almost positive that all these municipalities will be running rich data mining programs, and/or pay other companies to give them information about anyone who resides in their city was running a business.

They will then of course check this data and all these names against all their business licenses. If you run a business and you don't have a business license but you are doing business online, or it is mentioned on your Facebook page, you will not only have to pay the business license registration fee, you will also be charged with a penalty which could but be two or three times that amount. The cities will then have more revenue to spend by attacking small businesses just barely getting off the ground. Welcome to the future of data mining and your government. Please consider all this and think on it.



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Data Entry Services For Organization - Outsource Data Entry Services

It is unimportant that you have a small business or big organization to serve large audience. Information is an important aspect for any size or kind of company. In business, profitability is main focus. Currently, there is constant fluctuation in business world. Every business has to be dynamic with high tempo.

In such a high pressured business environment, quick accessibility of accurate and detailed information is essential. If you know more about your customer, industry, trend and other factor which affect your business, you can quickly compare your business and increase the value. To manage such requirements, data entry services are the best option. Typing services not only control all information but also control information management effectively.

For any business that wants to extract data from any source, data entry services are necessity. Different types of businesses require different services. Some organizations choose offline data typing services while other gives significance to online data typing services. The main purpose of data typing services are same - organizing data properly for future use. Data typing services also include image entry, book entry, card entry, hand-written entry, legal document entry, insurance claim entry and other.

The general idea about data entry services are entering data into business database. But it's not just; it also includes data collection, extraction and processing. Such typing task is very time consuming. These tasks can be performed quickly and efficiently by data typing expert. So, such professionals are in high demand.

Some years ago, it was assumed that only in-house personnel could really understand the company's products or services. But today, various business process outsourcing companies are having typing experts who are quite knowledgeable in almost every field of business. They can easily manage your requirements and deliver the best result.

Typing service companies can manage your information with higher efficiency and produce quicker result. In current scenario, business organizations do not waver to outsource the typing task. Now, most of the companies are outsourcing their typing task and getting benefit of higher productivity and profitability.

Business organizations have understood the importance of managing information and necessity of data entry services.

Bea Arthur is a quality controller at Data Entry India that provides Data Entry Services, Data Conversion Services and Data Processing Services. They are having more than 17 years of experience in data entry services.




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Data Mining - Critical for Businesses to Tap the Unexplored Market

Knowledge discovery in databases (KDD) is an emerging field and is increasingly gaining importance in today's business. The knowledge discovery process, however, is vast, involving understanding of the business and its requirements, data selection, processing, mining and evaluation or interpretation; it does not have any pre-defined set of rules to go about solving a problem. Among the other stages, the data mining process holds high importance as the task involves identification of new patterns that have not been detected earlier from the dataset. This is relatively a broad concept involving web mining, text mining, online mining etc.

What Data Mining is and what it is not?

The data mining is the process of extracting information, which has been collected, analyzed and prepared, from the dataset and identifying new patterns from that information. At this juncture, it is also important to understand what it is not. The concept is often misunderstood for knowledge gathering, processing, analysis and interpretation/ inference derivation. While these processes are absolutely not data mining, they are very much necessary for its successful implementation.

The 'First-mover Advantage'

One of the major goals of the data mining process is to identify an unknown or rather unexplored segment that had always existed in the business or industry, but was overlooked. The process, when done meticulously using appropriate techniques, could even make way for niche segments providing companies the first-mover advantage. In any industry, the first-mover would bag the maximum benefits and exploit resources besides setting standards for other players to follow. The whole process is thus considered to be a worthy approach to identify unknown segments.

The online knowledge collection and research is the concept involving many complications and, therefore, outsourcing the data mining services often proves viable for large companies that cannot devote time for the task. Outsourcing the web mining services or text mining services would save an organization's productive time which would otherwise be spent in researching.

The data mining algorithms and challenges

Every data mining task follows certain algorithms using statistical methods, cluster analysis or decision tree techniques. However, there is no single universally accepted technique that can be adopted for all. Rather, the process completely depends on the nature of the business, industry and its requirements. Thus, appropriate methods have to be chosen depending upon the business operations.

The whole process is a subset of knowledge discovery process and as such involves different challenges. Analysis and preparation of dataset is very crucial as the well-researched material could assist in extracting only the relevant yet unidentified information useful for the business. Hence, the analysis of the gathered material and preparation of dataset, which also considers industrial standards during the process, would consume more time and labor. Investment is another major challenge in the process as it involves huge cost on deploying professionals with adequate domain knowledge plus knowledge on statistical and technological aspects.

The importance of maintaining a comprehensive database prompted the need for data mining which, in turn, paved way for niche concepts. Though the concept has been present for years now, companies faced with ever growing competition have realized its importance only in the recent years. Besides being relevant, the dataset from where the information is actually extracted also has to be sufficient enough so as to pull out and identify a new dimension. Yet, a standardized approach would result in better understanding and implementation of the newly identified patterns.



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