Web Data Mining Software

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Data mining is the process by using certain algorithms, software and tools to retrieve, collect, analyze and report information (known as predictive analysis) from a large pool of data. Data mining is very useful today in which information is widely available. Information obtained from data mining is used for several applications for decision making related to direct marketing, e-commerce, customer relationship management, health, oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities.

Web data mining is the automated process of extracting data from the World Wide Web. Internet has extensive data on everything that can be used effectively to make intelligent decisions. However, taking and sorting through large databases is a daunting task. Therefore, there are certain data mining tools that help to make this easier. Tools can select relevant data and interpret it as needed.

There are many types of Web data mining: mining standards, verification of data and custom mining. Web data mining products can perform various functions that are very broad, including: search engine optimization and website promotion, double transformation and marketing of modular indicators for CRM, web log reporting, tracking patterns of website visitors, visitors to calculate the conversion ratio, reported the online customer behavior, analyze click-through, providing real-time log analysis, campaign tracking, street clicks, geographical setting, with a keyword search engine, report a web visitor analysis, content analysis, extract web events such as the results of campaigns, web traffic, etc.

There are several commercially available Web data mining and web mining applications using available software. Some of them are: AlterWind Log Analyzer Professional, Amadea Web Mining, ANGOSS KnowledgeWebMiner, Azure Web Log analyzer, the Blue Martini Customer Interaction System Micro Marketing module, ClickTracks, ConversionTrack from Antssoft, Datanautics, (formerly counting), eNuggets, (real- time middleware), LiveStats from DeepMetrix, Megaputer WebAnalyst, MicroStrategy Web Traffic Analysis Module, Web Analytics NetGenesis, family NetTracker, Nihuo Web Log Analyzer, prudsys ECOMMINER, Webhound SAS, SPSS Clementine Web mining, Weblog Expert 2.0 for Windows, WebTrends, suites Data Mining web traffic information, XAffinity (TM), XML Miner, 123LogAnalyzer. There is also a free version of web software such as Data Mining: AlterWind Log Analyzer Lite, Analog (from Dr. Stephen Turner), Visitator and WUM (Web Utilization Miner.)

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[1]http://EzineArticles.com/?expert=Ross_Bainbridge

Intelligent Data Mining

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Data mining has had a checkered history mainly due to technical constrains placed by limitations of software design and architecture. Most of the algorithms used in data mining are mature and have been around for over twenty years. The next challenges in data mining are not algorithmic but software design methodologies. Commonly used data mining algorithms are freely available and processes that optimize data mining computing speed are well documented.

Most early data mining software were spun off from academia and were built around an algorithm. The inability of early data mining software to integrate to external data sources and usability issues resulted in data mining being marginalized.

The cost associated with data mining is still unnecessarily high and often not cost effective. New standards in data extraction and better software platforms holds promise that the threshold barrier to entry will be reduced.
Data access standards such as OLE-DB, XML for Analysis and JSR will minimize the challenges for data access. Building a user friendly software interfaces for the end-user are the next steps in the evolution of data mining. A comparable analogy can be made with the increasing ease of use of OLAP client tools.
The J2EE and .NET software platforms offer a large spectrum of built-in APIs that enable smarter software applications.

DAT-A : Open Source Data Mining and OLAP on MySQL

DAT-A is an open source application that is built to allow intelligent data mining. By intelligent data mining, DAT-A's software architects are creating a highly decouple application that focuses the user's attention on the data mining results and not the data extraction or data modeling process. All data exchanges are in XML and SOAP to ensure interoperability.

An enterprise version is also being planned that is built on a BEA WebLogic Server that writes to a Web Services interface.
Presently MySQL does not have built-in data mining modules. DAT-A applies a data mining abstraction layer on MySQL. The business logic for controlling the data mining model and model training is written in the J2EE framework.

For the personal edition of DAT-A, the MySQL data mining application server is contained within the business logic developed on the J2EE framework layer. In the upcoming enterprise version, the business logic and data extraction controls will be hosted on BEA's WebLogic application server.

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[1]http://www.dwreview.com/Data_mining/Intelligent_DataMining.html

Data Mining

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Data Mining
By Ross Bainbridge

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.

Data Mining provides detaile

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d information on Data Mining, Data Mining Tutorials, Business Intelligence Data Mining, Web Data Mining and more. Data Mining is affiliated with Offshore Data Entry.

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business intelligence data mining

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Business Intelligence Data Mining
By Ross Bainbridge

Data mining can be technically defined as the automated extraction of hidden information from large databases for 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.

Data mining requires the use of mathematical algorithms and statistical techniques integrated with software tools. The final product is an easy-to-use software package that can be used even by non-mathematicians to effectively analyze the data they have. Data Mining is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, fraud detection, web site personalization, e-commerce, healthcare, customer relationship management, financial services and telecommunications.

Business intelligence data mining 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, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. BI uses various technologies like data mining, scorecarding, data warehouses, text mining, decision support systems, executive information systems, management information systems and geographic information systems for analyzing useful information for business decision making.

Business intelligence is a broader arena of decision-making that uses data mining as one of the tools. In fact, the use of data mining in BI makes the data more relevant in application. There are several kinds of data mining: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining, that are all used in business intelligence applications.

Some data mining tools used in BI 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 and so on.

Data Mining provides detailed information on Data Mining, Data Mining Tutorials, Business Intelligence Data Mining, Web Data Mining and more. Data Mining is affiliated with Offshore Data Entry.

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