Hewlett-Packard Development Company, L.p.

James Richard Stinger

Henri Jacques Suermondt

George H Forman

Maria G Castellanos

Todd Mitchell Goin

Douglas William Steele

Nina Mishra

Randall Bruce Campbell

Thomas Elliott Fawcett

Michael J Lemon

George H Fortnan

A related feature determination system determines a feature related to an indication of a concept. A set of positive cases and a set of negative case are selected using the indication of a concept. A classifier is induced for the concept using the set of positive cases in a manner blinded to the indication of a concept. The set of negative cases is applied to the classifier. A feature related to the indication of a concept is determined using results of applying the classifier to the set of negative cases.

Determining a feature related to an indication of a concept using a classifier

A system or method for minimally predictive feature identification is disclosed. For information management, an information collection including a set of features is received. A set of prediction values indicating a degree to which a first feature within the set of features predicts other features in the set is generated. The first feature as a minimally predictive feature is identified if each of the prediction values is within a predetermined range of threshold values.

System and method for minimally predictive feature identification

The present invention is a system or method for assisting in the maintenance and servicing of computers. The system, for example, comprises a cluster database containing information relating to one or more computer clusters, the information relating to each cluster comprising one or more cluster descriptions and one or more representations of configuration information values that characterize typical computer members of the cluster, and also a tracker database containing configuration information values gathered from and relating to one or more computers. The system further comprises a computer and cluster comparison routine having access to the cluster and tracker databases that compares one or more of the representations of configuration information values relating to one or more of the clusters to one or more of the corresponding configuration information values gathered from at least one given computer and that then indicates how closely the given computer's configuration conforms with the configuration of typical computer members of the one or more clusters.

Method and system for comparing individual computers to cluster representations of their peers

A system and method for category discovery is disclosed. The method discloses: receiving an information collection including a set of strings; identifying positively predictive pairs of strings; identifying negatively predictive pairs of strings; joining positively predictive pairs of strings into a category; and splitting negatively predictive pairs of strings into different categories. The system discloses various elements, means and instructions for performing the method.

System and method for category discovery

A system and method for category organization is disclosed. The method discloses: receiving an information collection including a set of strings; identifying positively predictive pairs of strings; identifying negatively predictive pairs of strings; joining positively predictive pairs of strings into common categories; splitting negatively predictive pairs of strings into different categories; and organizing the categories using the negatively predictive pairs of strings. The system discloses various elements, means and instructions for performing the method.

System and method for category organization

A method and system for identifying clusters of similarly-configured computers. The method, for example, comprises gathering system and business configuration information values from a set of computers. Next, at least a portion of the system and business configuration information values gathered from the set of computers is analyzed along with the numbers of computers that are configured in various ways. Then, using the result of the analysis as a guide, logical expressions of configuration information values are selected for use as definitions of one or more clusters of similarly-configured computers, the selection process generally maximizing the number of computers included within each cluster and also generally maximizing the number of configuration information value specifications included within the definition of each cluster. Then, for one or more clusters so defined and selected, one or more statistics or limit values are generated from one or more configuration information values or both gathered from the subset of computers included in the one or more clusters.

Method and system for clustering computers into peer groups and comparing individual computers to their peers

A method and system of normalizing dirty text in a document. The present invention creates a thesaurus that evolves over time as new document collections are analyzed. This thesaurus, which is used by an editor, contains standard terms and phrases, and their corresponding variations of these standard terms and phrases. Documents are run through this editor and misspelled words or phrases, joined words, and ad hoc abbreviations are replaced with standard terms from the thesaurus. The present invention also enables normalization of documents in cases where a list of standard terms must be inferred from the corpus of the document. The normalizer will facilitate data mining applications which can not function properly with dirty text, resulting in more accurate analysis of documents. Over time, as the thesaurus evolves, collecting more words and phrases, the process of generating the thesaurus will become more automated.

Method and system for normalizing dirty text in a document

A method and system for mining a document containing dirty text. Dirty text is removed or replaced and the document is processed using a variety of text mining techniques. In one embodiment, dirty text removal and replacement occurs in two stages. In the first stage, a general cleaning occurs on all documents without regard to what domain they belong to or the mining task to be performed. In the second stage, document cleaning is more specific to the anomalies of the domain and the mining task to be performed. In the third stage, the document is processed using a variety of data mining techniques according to the mining task. In one embodiment, the present invention scores and ranks sentences in a document according to their relevance, extracts the highest ranked sentences, and presents a summary. The present invention allows users to leverage existing domain knowledge and can be customized according the domain and task requirements.

Method and system for mining a document containing dirty text

Embodiments of the present invention are directed to a method for content mining of semi-structured documents. In one embodiment, a semi-structured document is first converted from a document-type specific format such as HTML or PDF, to a document-type independent format such as XML. The document formatting, which contains basic level information about the document's structure, is then analyzed by a series of modules to develop a higher level understanding of the document's structure. These modules append information to the document describing the features which collectively comprise the higher level document structure. The appended information facilitates finding specified information within the document when content mining is performed.

Method for content mining of semi-structured documents

A method for automatically detecting table data in a document that is described by a page definition language and converting the table data into a markup language representation. The document may have one or more pages. The page definition language description of the document provides a list of words, the position of the each on a page with respect to a predetermined reference point, and the size of each word. The present invention automatically identifies table data in the document by utilizing one or more table-identifying features. A first table-identifying feature may be the number of word clusters on a line. A second table-identifying feature may be the vertical alignment of word clusters between lines. A third table-identifying feature may be the changes in text density or space density between lines.

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Palo Alto, CA, United States of America

James Richard Stinger