Clustering Engine Service

Root Node:

Input data:

http://en.wikipedia.org/wiki/Text_mining<FIELD>One of the largest text mining applications that exists is probably the classified ECHELON surveillance system. Additionally, many text mining software packages such as AeroText , Attensity , SPSS and Expert System are marketed towards security applications, particularly analysis of plain text sources such as Internet news.<FIELD>Text_mining data_mining text_mining_applications text_mining_software text_analytics Text_Analysis <RECORD>http://www.spss.com/text_mining_for_clementine/<FIELD>Text Mining for Clementine is a text mining workbench that enables you to extract key concepts, sentiments, and relationships from textual or "unstructured" data and convert them to a structured format that can be used to create predictive models. You can access Text Mining for Clementine directly from the interface of SPSS Inc.'s industry-leading data mining workbench, Clementine<FIELD>Text_Mining Predictive_Text Contact_SPSS_Sales Predictive_Models text_mining_workbench SPSS_Inc data_mining_workbench data_mining <RECORD>http://www.scils.rutgers.edu/~msharp/text_mining.htm<FIELD>Chowdhury (1999) describes template mining as a form of information extraction using NLP 'to extract data directly from the text if either the data and/or text surrounding the data form recognizable patterns. When text matches a template, the system extracts data according to the instructions associated with that template.' Chowdury traces its history from the mid-1960s Linguistic String Project at New York University, where 'fact retrieval' was conducted against template data mined from natural language text, up to its current (1999) use in the AltaVista and<FIELD>Text_Mining information_retrieval data_mining natural_language_processing natural_language_text new_knowledge data_extraction text_data text_data_mining information_extraction information_processing text_mining_system text_analysis NLP_system natural_language new_information <RECORD>http://www.sra.com/services/index.asp?id=172<FIELD>SRA begins by working with an organization to understand its goals and to identify the critical information it needs to obtain from unstructured text sources.? We analyze sample text and determine the appropriate text mining techniques to be used.? SRA defines an appropriate text mining architecture and how it will best fit with an IT infrastructure.? We provide text mining tool recommendations and recommendations for other analytical tools that can take advantage of the text mining results, such as link analysis tools, OLAP tools, and data mining tools.<FIELD>Text_Mining_Solutions Text_mining unstructured_text unstructured_data critical_information data_mining_tools text_mining_tools<RECORD>http://www.spss.com/predictive_text_analytics/index.htm?source=homepage&hpzone=tech<FIELD>SPSS has been a pioneer in Predictive Text Analytics. Our solution combines the natural language processing (NLP) linguistic technologies of our LexiQuest text mining products with the advanced data mining capabilities of our data mining workbench, Clementine. Our solution uses natural language processing (NLP) linguistic technologies to uncover concepts in text. You can then combine text concepts with other data in predictive models. Read more about Predictive Analytics products here .<FIELD>Predictive_Text_Analytics Contact_SPSS_Sales Data_Mining text_mining SPSS_Inc Uncover_concepts predictive_models Predictive_Analytics <RECORD>http://www.cs.waikato.ac.nz/~nzdl/textmining/<FIELD>Stuart Yeates and Ian H. Witten . On Tag Insertion and it's Complexity . International Workshop on Text and Web Mining in conjunction with the Sixth Pacific Rim International Conference on Artifical Intelligence (PRICAI 2000) , pages 52-63 28 August 2000, Melbourne, Australia. ISBN 0 7300 2511 X.<FIELD>Text_Mining language_text computer_science Web_mining information_extraction academic_staff_member Stuart_Yeates Data_Compression_Conference Martin_Cohn IEEE_Press Los_Alamitos International_Workshop International_Conference Craig_Nevill-Manning Data_Mining Data_Mining_Page Machine_Learning <RECORD>http://www.sas.com/technologies/analytics/datamining/<FIELD>Every organization accumulates huge volumes of data from a variety of sources on a daily basis. Data mining is the process of selecting, exploring and modeling large amounts of raw data to uncover previously unknown trends and patterns. Text mining applies the same analysis techniques to text-based documents. The knowledge gleaned from data and text mining can be used to fuel strategic decision making.<FIELD>Text_Mining Data_Mining raw_data Customer_Data_Mining <RECORD>http://people.ischool.berkeley.edu/~hearst/papers/acl99/acl99-tdm.html<FIELD>In this paper I will first define data mining, information access, and corpus-based computational linguistics, and then discuss the relationship of these to text data mining. The intent behind these contrasts is to draw attention to exciting new kinds of problems for computational linguists. I describe examples of what I consider to be real text data mining efforts and briefly outline our recent ideas about how to pursue exploratory data analysis over text.<FIELD>Text_Data_Mining data_mining text_collections data_analysis text_data new_information text_analysis text_mining <RECORD>http://people.ischool.berkeley.edu/~hearst/text-mining.html<FIELD>The difference between regular data mining and text mining is that in text mining the patterns are extracted from natural language text rather than from structured databases of facts. Databases are designed for programs to process automatically; text is written for people to read. We do not have programs that can 'read' text and will not have such for the forseeable future. Many researchers think it will require a full simulation of how the mind works before we can write programs that read the way people do.<FIELD>Text_Mining data_mining write_programs <RECORD>http://www.statsoft.com/textbook/sttextmin.html<FIELD>To reiterate, text mining can be summarized as a process of 'numericizing' text. At the simplest level, all words found in the input documents will be indexed and counted in order to compute a table of documents and words, i.e., a matrix of frequencies that enumerates the number of times that each word occurs in each document. This basic process can be further refined to exclude certain common words such as 'the' and 'a' (stop word lists) and to combine different grammatical forms of the same words such as 'traveling,' 'traveled,' 'travel,' etc. ( stemming ). However, once a table of (unique) words (terms) by documents has been derived, all standard statistical and data mining techniques can be applied to derive dimensions or clusters of words or documents, or to identify 'important' words or terms that best predict another outcome variable of interest.<FIELD>Text_Mining Word_Frequencies Semantic_Indexing Data_Mining input_documents large_numbers extracted_words document_frequencies different_words semantic_dimensions <RECORD>http://www.sas.com/technologies/analytics/datamining/textminer/<FIELD>SAS Text Miner provides a rich suite of tools for discovering and extracting knowledge from text documents. It transforms textual data into a usable, intelligible format that facilitates classifying documents, finding explicit relationships or associations between documents, and clustering documents into categories. It's the first mining solution that tightly integrates text-based information with structured data for improved analyses and decision making.<FIELD>Data_Mining Business_Intelligence Text_Mining SAS_Text_Miner <RECORD>http://www.kdnuggets.com/software/text.html<FIELD>Basis Technology , provides natural language processing technology for the analysis of unstructured multilingual text.<FIELD>Text_Analysis Text_Mining natural_language unstructured_text unstructured_information semantic_analysis free_text <RECORD>http://opentextmining.org/<FIELD>The Open Text Mining Interface (OTMI) is an initiative from Nature Publishing Group (NPG). It aims to enable scholarly publishers, among others, to disclose their full text for indexing and text-mining purposes but without giving it away in a form that is readily human-readable. It provides for a range of structured disclosure options, from word vectors (lists of word occurrences with frequency counts) and the presentation of text 'snippets' out of narrative order, to the presentation of full text in 'raw' or 'reduced' form.<FIELD>Main_Page Open_Text_Mining_Interface Text_Mining <RECORD>http://www.cs.utk.edu/tmw07/<FIELD>This year's program includes an Anomaly Detection/Text Mining competition sponsored by NASA Ames Research Center. The competition is open to all SDM07 attendees. Winners will receive cash prizes and an invitation to present their work at the workshop. Note that scoring software used to evaluate entries is provided (posted on January 10 at 10:30am EST). Please email all contest entries to the address below no later than 2pm EST on Friday, January 12, 2007 deadline has passed :<FIELD>State_University Hewlett-Packard_Laboratories Computer_Science TN_37996-3450 cs_DOT_utk_DOT_edu text_mining <RECORD>http://www.cs.cmu.edu/~dunja/WshKDD2000.html<FIELD>The objective of this workshop is to enable presentation and exchange of ideas on various aspects of Text Mining. Our desire is to facilitate communication among researchers and practitioners from related and complementary research areas, who are working on similar problems but with possibly different focus and problem solving approaches. More precisely, we invite papers from the four areas:<FIELD>Text_Mining Information_Extraction text_categorization Stefan_Institute Microsoft_Research_Ltd Microsoft_Research <RECORD>http://datamining.typepad.com/data_mining/text_mining/index.html<FIELD>Charles Armstrong, CEO of Trampoline Systems, pointed me to Enron Explorer - a technology demonstration. Email data is hard to publish and share due to obvious issues of privacy. Consequently, it is hard to create data sets that can be used openly for text mining, natural language processing and other language and social research and that can be passed around the scientific community. Thankfully, the boys and girls of Enron came to our rescue and, via their prosecution, inadvertently provided public access to a huge amount of email data.<FIELD>Text_Mining Email_data <RECORD>http://filebox.vt.edu/users/wfan/text_mining.html<FIELD>Prof. Richard Belew at UCSD and his collection on Machine Learning and Information Retrieval<FIELD>text_mining information_retrieval machine_learning <RECORD>http://www.computerworld.com/databasetopics/businessintelligence/story/0,10801,93968,00.html<FIELD>To make the functionality more broadly available while they tackle usability problems, vendors are incorporating text mining tools as a background function to improve the effectiveness of more familiar search or document management applications.<FIELD>Business_Intelligence Data_Mining Security_Management Data_Management Text_mining_tools text_mining BI_software SAS_Text Text_Tools Information_Management Computerworld_Inc Text_mining_Desktop <RECORD>http://datamining.typepad.com/data_mining/<FIELD>Sentimetrix : Provides sentiment mining technologies to social media analysis shops.<FIELD>Social_Media Opinion_Mining sentiment_mining text_mining <RECORD>http://www.ebi.ac.uk/Rebholz/<FIELD>Currently the group is involved in a number of initiatives that prepare text mining solutions for their use in the public (refer to Whatizit ). One initiative started at the ISMB 2006 in Fortaleza, Brasil , and focuses on the development of solutions that eases the flow of information provided through the scientific literature into other electronic data repositories.<FIELD>Rebholz_Group inline_frames text_mining<RECORD>

Service Documentation

Service endpoint: http://www.ultimate-research-assistant.com/oem/clusteringengine.asp
Protocol: REST (XML over HTTP)
Methods: GET and POST (POST is greatly preferred given the volume of data sent over the typical request to this service)

Service Parameters

Parameter Name	Description	Data Type	Default Value
root	The label attached to the root node for your taxonomy. If not specified or zero-length, the label "Root Node" is used.	String	"Root Node"
indata	A data structure comprising a concatenation of the individual instance data to be clustered into a hierarchical data structure (taxonomy). Then clustering engine returns an taxonomy conforming to the Freemind version 0.8.1 XML format. Each record in the expected data structure takes the following form (see example above): Page URL or unique ID (Mandatory value, should be unique for each record)<FIELD>Text snippet (Can be zero-length; however, highly recommended as it used by the clustering engine to select or discard low-occurrence concepts)<FIELD>Space delimited list of concepts or tags (multi-word tags should use underscore rather than space). Mandatory value - these concepts or tags are what is actually clustered by the service<RECORD>	String	Not applicable (required value)