Cluster-Based Adaptive Information Retrieval System
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Maria Zemankova', 'PO_EMAI': None, 'PO_PHON': None}
A retrieval system model that leads to an integrated approach in                which both local and global feedback naturally blend into a                     unified process is proposed.  The novelty of this approach is                   that the relevance feedback of a particular query instance as                   well as the accumulated knolwdge from past queries are directly                 related to the performance of the present query.  This model                    represents a cluster-based approach to information retrieval.                   The cluster-based approach is developed around a clustering                     technique that captures the users concept of closeness between                  documents.  Since the clustering technique to support the                       retrieval process is developed.  The principal advantage of the                 proposed model is that the retrieval performance can be directly                influenced by the optimization criterion employed during                        clustering.  The significance of our approach lies in the fact                  that document representation and the measure of similarity among                doucments do not have to be prespecified in an ad hoc manner.                   Instead such design alternatives can be dictated by                             cluster-scope of application than just information retrieval.
[{'FirstName': 'Vijay', 'LastName': 'Raghavan', 'PI_MID_INIT': 'V', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Vijay V Raghavan', 'EmailAddress': 'raghavan@louisiana.edu', 'NSF_ID': '000359473', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Jitender', 'LastName': 'Deogun', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jitender S Deogun', 'EmailAddress': 'deogun@cse.unl.edu', 'NSF_ID': '000313607', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
{'Name': 'University of Louisiana at Lafayette', 'CityName': 'LAFAYETTE', 'ZipCode': '705032014', 'PhoneNumber': '3374825811', 'StreetAddress': '104 E UNIVERSITY AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Louisiana', 'StateCode': 'LA', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'LA03', 'ORG_UEI_NUM': 'C169K7T4QZ96', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF LOUISIANA AT LAFAYETTE', 'ORG_PRNT_UEI_NUM': 'C169K7T4QZ96'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '685500', 'Text': 'INFORMATION & KNOWLEDGE MANAGE'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8805875.xml'}