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You searched for +publisher:"University of Cincinnati" +contributor:("Bhatnagar, Dr. Raj"). Showing records 1 – 21 of 21 total matches.

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University of Cincinnati

1. Young, Barrington R. St. A. Efficient Algorithms for Data Mining with Federated Databases.

Degree: PhD, Engineering : Computer Science, 2007, University of Cincinnati

 The internet era and high speed networks have ushered in the capabilities to have ready access to large amounts of geographically distributed data. Individuals, businesses,… (more)

Subjects/Keywords: Computer Science; Federated database; Vertical partition; Arbitrary attribute overlap; Covariance Matrix; k-Nearest Neighbors; Euclidean distance; Cluster

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APA (6th Edition):

Young, B. R. S. A. (2007). Efficient Algorithms for Data Mining with Federated Databases. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179332091

Chicago Manual of Style (16th Edition):

Young, Barrington R St A. “Efficient Algorithms for Data Mining with Federated Databases.” 2007. Doctoral Dissertation, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179332091.

MLA Handbook (7th Edition):

Young, Barrington R St A. “Efficient Algorithms for Data Mining with Federated Databases.” 2007. Web. 16 Jun 2019.

Vancouver:

Young BRSA. Efficient Algorithms for Data Mining with Federated Databases. [Internet] [Doctoral dissertation]. University of Cincinnati; 2007. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179332091.

Council of Science Editors:

Young BRSA. Efficient Algorithms for Data Mining with Federated Databases. [Doctoral Dissertation]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179332091


University of Cincinnati

2. SHENCOTTAH K.N., KALYANKUMAR. FINDING CLUSTERS IN SPATIAL DATA.

Degree: MS, Engineering : Computer Science, 2007, University of Cincinnati

 Spatial data mining is the discovery of patterns in spatial databases. The driving factor for research in spatial data mining is the increase in collection… (more)

Subjects/Keywords: Computer Science; clusters; Spatial data mining; Quad-Tree; Spatial clustering

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APA (6th Edition):

SHENCOTTAH K.N., K. (2007). FINDING CLUSTERS IN SPATIAL DATA. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337

Chicago Manual of Style (16th Edition):

SHENCOTTAH K.N., KALYANKUMAR. “FINDING CLUSTERS IN SPATIAL DATA.” 2007. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337.

MLA Handbook (7th Edition):

SHENCOTTAH K.N., KALYANKUMAR. “FINDING CLUSTERS IN SPATIAL DATA.” 2007. Web. 16 Jun 2019.

Vancouver:

SHENCOTTAH K.N. K. FINDING CLUSTERS IN SPATIAL DATA. [Internet] [Masters thesis]. University of Cincinnati; 2007. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337.

Council of Science Editors:

SHENCOTTAH K.N. K. FINDING CLUSTERS IN SPATIAL DATA. [Masters Thesis]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337


University of Cincinnati

3. NARAYANASWAMY, SHRIRAM. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.

Degree: MS, Engineering : Computer Science, 2007, University of Cincinnati

 In today’s consumer driven world, people are faced with the problem of plenty. Choices abound everywhere, be it in movies, books or music. Recommender systems… (more)

Subjects/Keywords: Computer Science; collaborative filtering, recommender systems; lattice, concept, algorithm, Jester, Movielens

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APA (6th Edition):

NARAYANASWAMY, S. (2007). A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016

Chicago Manual of Style (16th Edition):

NARAYANASWAMY, SHRIRAM. “A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.” 2007. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

MLA Handbook (7th Edition):

NARAYANASWAMY, SHRIRAM. “A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.” 2007. Web. 16 Jun 2019.

Vancouver:

NARAYANASWAMY S. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. [Internet] [Masters thesis]. University of Cincinnati; 2007. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

Council of Science Editors:

NARAYANASWAMY S. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. [Masters Thesis]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016


University of Cincinnati

4. RAMAMURTHY, SASTHAKUMAR. TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS.

Degree: MS, Engineering : Computer Science, 2007, University of Cincinnati

 Streaming data may consist of multiple drifting concepts each having its own under- lying data distribution. We present an ensemble learning based approach to handle… (more)

Subjects/Keywords: Computer Science

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APA (6th Edition):

RAMAMURTHY, S. (2007). TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577

Chicago Manual of Style (16th Edition):

RAMAMURTHY, SASTHAKUMAR. “TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS.” 2007. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577.

MLA Handbook (7th Edition):

RAMAMURTHY, SASTHAKUMAR. “TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS.” 2007. Web. 16 Jun 2019.

Vancouver:

RAMAMURTHY S. TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS. [Internet] [Masters thesis]. University of Cincinnati; 2007. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577.

Council of Science Editors:

RAMAMURTHY S. TRACKING RECURRENT CONCEPT DRIFT IN STREAMING DATA USING ENSEMBLE CLASSIFIERS. [Masters Thesis]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196103577


University of Cincinnati

5. YARDI, APARNA ARVIND. CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL.

Degree: MS, Engineering : Computer Science, 2006, University of Cincinnati

 The area of Information Retrieval in the Computer Science concerns itself with the retrieval of information from a collection of documents. There are various techniques… (more)

Subjects/Keywords: Computer Science; Information Retrieval; Concept Lattice

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APA (6th Edition):

YARDI, A. A. (2006). CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1152832274

Chicago Manual of Style (16th Edition):

YARDI, APARNA ARVIND. “CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL.” 2006. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1152832274.

MLA Handbook (7th Edition):

YARDI, APARNA ARVIND. “CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL.” 2006. Web. 16 Jun 2019.

Vancouver:

YARDI AA. CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL. [Internet] [Masters thesis]. University of Cincinnati; 2006. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1152832274.

Council of Science Editors:

YARDI AA. CONCEPT BASED INFORMATION ORGANIZATION AND RETRIEVAL. [Masters Thesis]. University of Cincinnati; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1152832274


University of Cincinnati

6. Kurra, Goutham. Pattern Recognition in Large Dimensional and Structured Datasets.

Degree: MS, Engineering : Computer Science, 2002, University of Cincinnati

 Gene expression datasets obtained from DNA microarrays are examples of large-dimensional and structured datasets. In this thesis, we approach the task of applying pattern recognition… (more)

Subjects/Keywords: Computer Science; feature selection; partial profile clustering; pattern recognition; clustering structured data; gene expression; data analysis

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APA (6th Edition):

Kurra, G. (2002). Pattern Recognition in Large Dimensional and Structured Datasets. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1014322308

Chicago Manual of Style (16th Edition):

Kurra, Goutham. “Pattern Recognition in Large Dimensional and Structured Datasets.” 2002. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1014322308.

MLA Handbook (7th Edition):

Kurra, Goutham. “Pattern Recognition in Large Dimensional and Structured Datasets.” 2002. Web. 16 Jun 2019.

Vancouver:

Kurra G. Pattern Recognition in Large Dimensional and Structured Datasets. [Internet] [Masters thesis]. University of Cincinnati; 2002. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1014322308.

Council of Science Editors:

Kurra G. Pattern Recognition in Large Dimensional and Structured Datasets. [Masters Thesis]. University of Cincinnati; 2002. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1014322308


University of Cincinnati

7. VETTEL, LYNNE ANN. LEARNING DETERMINISTIC FINITE AUTOMATA TO CAPTURE TEMPORAL PATTERNS.

Degree: MS, Engineering : Computer Science, 2002, University of Cincinnati

 This thesis considers the problem of discovering temporal patterns in time series data using deterministic finite automata (DFAs) as a description of temporal hypotheses. The… (more)

Subjects/Keywords: Computer Science; temporal patterns; machine learning; fallible teacher; incomplete knowledge

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APA (6th Edition):

VETTEL, L. A. (2002). LEARNING DETERMINISTIC FINITE AUTOMATA TO CAPTURE TEMPORAL PATTERNS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037999729

Chicago Manual of Style (16th Edition):

VETTEL, LYNNE ANN. “LEARNING DETERMINISTIC FINITE AUTOMATA TO CAPTURE TEMPORAL PATTERNS.” 2002. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037999729.

MLA Handbook (7th Edition):

VETTEL, LYNNE ANN. “LEARNING DETERMINISTIC FINITE AUTOMATA TO CAPTURE TEMPORAL PATTERNS.” 2002. Web. 16 Jun 2019.

Vancouver:

VETTEL LA. LEARNING DETERMINISTIC FINITE AUTOMATA TO CAPTURE TEMPORAL PATTERNS. [Internet] [Masters thesis]. University of Cincinnati; 2002. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037999729.

Council of Science Editors:

VETTEL LA. LEARNING DETERMINISTIC FINITE AUTOMATA TO CAPTURE TEMPORAL PATTERNS. [Masters Thesis]. University of Cincinnati; 2002. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037999729


University of Cincinnati

8. KHEDR, AHMED MOHAMED. DESIGN OF DECOMPOSABLE ALGORITHMS FOR DISTRIBUTED DATABASES.

Degree: PhD, Engineering : Computer Science and Engineering, 2003, University of Cincinnati

 Most computer algorithms have been designed for situations in which all relevant data is stored at a single Computer site. This is the classical model… (more)

Subjects/Keywords: Computer Science; decomposable algorithm; distributed database; clustering; graph

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APA (6th Edition):

KHEDR, A. M. (2003). DESIGN OF DECOMPOSABLE ALGORITHMS FOR DISTRIBUTED DATABASES. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1044894428

Chicago Manual of Style (16th Edition):

KHEDR, AHMED MOHAMED. “DESIGN OF DECOMPOSABLE ALGORITHMS FOR DISTRIBUTED DATABASES.” 2003. Doctoral Dissertation, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1044894428.

MLA Handbook (7th Edition):

KHEDR, AHMED MOHAMED. “DESIGN OF DECOMPOSABLE ALGORITHMS FOR DISTRIBUTED DATABASES.” 2003. Web. 16 Jun 2019.

Vancouver:

KHEDR AM. DESIGN OF DECOMPOSABLE ALGORITHMS FOR DISTRIBUTED DATABASES. [Internet] [Doctoral dissertation]. University of Cincinnati; 2003. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1044894428.

Council of Science Editors:

KHEDR AM. DESIGN OF DECOMPOSABLE ALGORITHMS FOR DISTRIBUTED DATABASES. [Doctoral Dissertation]. University of Cincinnati; 2003. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1044894428


University of Cincinnati

9. BIAN, HAIYUN. FINDING INTERESTING SUBSPACE CLUSTERS FROM HIGH DIMENSIONAL DATASETS.

Degree: PhD, Engineering : Computer Science, 2006, University of Cincinnati

 Data mining focuses on finding previously unknown yet potentially useful, hidden patterns from large amounts of data. Clustering is one of the most commonly used… (more)

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APA (6th Edition):

BIAN, H. (2006). FINDING INTERESTING SUBSPACE CLUSTERS FROM HIGH DIMENSIONAL DATASETS. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1161732284

Chicago Manual of Style (16th Edition):

BIAN, HAIYUN. “FINDING INTERESTING SUBSPACE CLUSTERS FROM HIGH DIMENSIONAL DATASETS.” 2006. Doctoral Dissertation, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1161732284.

MLA Handbook (7th Edition):

BIAN, HAIYUN. “FINDING INTERESTING SUBSPACE CLUSTERS FROM HIGH DIMENSIONAL DATASETS.” 2006. Web. 16 Jun 2019.

Vancouver:

BIAN H. FINDING INTERESTING SUBSPACE CLUSTERS FROM HIGH DIMENSIONAL DATASETS. [Internet] [Doctoral dissertation]. University of Cincinnati; 2006. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1161732284.

Council of Science Editors:

BIAN H. FINDING INTERESTING SUBSPACE CLUSTERS FROM HIGH DIMENSIONAL DATASETS. [Doctoral Dissertation]. University of Cincinnati; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1161732284


University of Cincinnati

10. SHINDE, KAUSTUBH ARUN. FUNCTION COMPUTING IN VERTICALLY PARTITIONED DISTRIBUTED DATABASES.

Degree: MS, Engineering : Computer Science, 2006, University of Cincinnati

 Advances in database and storage technology and need to manage constant flow of information have necessitated use of databases for every organization. These databases are… (more)

Subjects/Keywords: Computer Science; distributed databases; function computing

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APA (6th Edition):

SHINDE, K. A. (2006). FUNCTION COMPUTING IN VERTICALLY PARTITIONED DISTRIBUTED DATABASES. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163574762

Chicago Manual of Style (16th Edition):

SHINDE, KAUSTUBH ARUN. “FUNCTION COMPUTING IN VERTICALLY PARTITIONED DISTRIBUTED DATABASES.” 2006. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163574762.

MLA Handbook (7th Edition):

SHINDE, KAUSTUBH ARUN. “FUNCTION COMPUTING IN VERTICALLY PARTITIONED DISTRIBUTED DATABASES.” 2006. Web. 16 Jun 2019.

Vancouver:

SHINDE KA. FUNCTION COMPUTING IN VERTICALLY PARTITIONED DISTRIBUTED DATABASES. [Internet] [Masters thesis]. University of Cincinnati; 2006. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163574762.

Council of Science Editors:

SHINDE KA. FUNCTION COMPUTING IN VERTICALLY PARTITIONED DISTRIBUTED DATABASES. [Masters Thesis]. University of Cincinnati; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163574762


University of Cincinnati

11. JHAVER, RISHI. DISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS.

Degree: MS, Engineering : Computer Science, 2003, University of Cincinnati

 We work with temporal data stored in distributed databases that are spread over a region. We have considered a sensor network where a lot of… (more)

Subjects/Keywords: Computer Science; distributed data sets; data aggregation; in-network aggregation; temporal databases; sensor data sets

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APA (6th Edition):

JHAVER, R. (2003). DISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069437745

Chicago Manual of Style (16th Edition):

JHAVER, RISHI. “DISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS.” 2003. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069437745.

MLA Handbook (7th Edition):

JHAVER, RISHI. “DISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS.” 2003. Web. 16 Jun 2019.

Vancouver:

JHAVER R. DISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS. [Internet] [Masters thesis]. University of Cincinnati; 2003. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069437745.

Council of Science Editors:

JHAVER R. DISCOVERY OF LINEAR TRAJECTORIES IN GEOGRAPHICALLY DISTRIBUTED DATASETS. [Masters Thesis]. University of Cincinnati; 2003. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069437745


University of Cincinnati

12. BATRA, SHALINI. DISCOVERY OF CLUSTERS IN SPATIAL DATABASES.

Degree: MS, Engineering : Computer Science, 2003, University of Cincinnati

 Spatial data mining is discovery of interesting relationships and Characteristics that may exist implicitly in databases. Data mining systems aim to discover patterns, find unexpected… (more)

Subjects/Keywords: Computer Science; spatial data mining; clustering; quad-tree; clusters

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APA (6th Edition):

BATRA, S. (2003). DISCOVERY OF CLUSTERS IN SPATIAL DATABASES. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069701237

Chicago Manual of Style (16th Edition):

BATRA, SHALINI. “DISCOVERY OF CLUSTERS IN SPATIAL DATABASES.” 2003. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069701237.

MLA Handbook (7th Edition):

BATRA, SHALINI. “DISCOVERY OF CLUSTERS IN SPATIAL DATABASES.” 2003. Web. 16 Jun 2019.

Vancouver:

BATRA S. DISCOVERY OF CLUSTERS IN SPATIAL DATABASES. [Internet] [Masters thesis]. University of Cincinnati; 2003. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069701237.

Council of Science Editors:

BATRA S. DISCOVERY OF CLUSTERS IN SPATIAL DATABASES. [Masters Thesis]. University of Cincinnati; 2003. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069701237


University of Cincinnati

13. RAJSHIVA, ANSHUMAAN. MINING STRUCTURED SETS OF SUBSPACES FROM HIGH DIMENSIONAL DATA.

Degree: MS, Engineering : Computer Science, 2004, University of Cincinnati

 Data mining is the process of extracting possibly unknown and potentially useful information from databases. Data mining algorithms are used in many applications in the… (more)

Subjects/Keywords: Datamining; Subspace Clustering; Complete Subspace

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APA (6th Edition):

RAJSHIVA, A. (2004). MINING STRUCTURED SETS OF SUBSPACES FROM HIGH DIMENSIONAL DATA. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085667702

Chicago Manual of Style (16th Edition):

RAJSHIVA, ANSHUMAAN. “MINING STRUCTURED SETS OF SUBSPACES FROM HIGH DIMENSIONAL DATA.” 2004. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085667702.

MLA Handbook (7th Edition):

RAJSHIVA, ANSHUMAAN. “MINING STRUCTURED SETS OF SUBSPACES FROM HIGH DIMENSIONAL DATA.” 2004. Web. 16 Jun 2019.

Vancouver:

RAJSHIVA A. MINING STRUCTURED SETS OF SUBSPACES FROM HIGH DIMENSIONAL DATA. [Internet] [Masters thesis]. University of Cincinnati; 2004. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085667702.

Council of Science Editors:

RAJSHIVA A. MINING STRUCTURED SETS OF SUBSPACES FROM HIGH DIMENSIONAL DATA. [Masters Thesis]. University of Cincinnati; 2004. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085667702


University of Cincinnati

14. KAKKAR, SHAGUN. METHODOLOGY FOR CLUSTERING SPATIO-TEMPORAL DATABASES.

Degree: MS, Engineering : Computer Science, 2004, University of Cincinnati

 Data mining aims to discover patterns and extract useful information recorded in databases. Spatial data mining and temporal data mining are two important branches that… (more)

Subjects/Keywords: Computer Science

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APA (6th Edition):

KAKKAR, S. (2004). METHODOLOGY FOR CLUSTERING SPATIO-TEMPORAL DATABASES. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085712249

Chicago Manual of Style (16th Edition):

KAKKAR, SHAGUN. “METHODOLOGY FOR CLUSTERING SPATIO-TEMPORAL DATABASES.” 2004. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085712249.

MLA Handbook (7th Edition):

KAKKAR, SHAGUN. “METHODOLOGY FOR CLUSTERING SPATIO-TEMPORAL DATABASES.” 2004. Web. 16 Jun 2019.

Vancouver:

KAKKAR S. METHODOLOGY FOR CLUSTERING SPATIO-TEMPORAL DATABASES. [Internet] [Masters thesis]. University of Cincinnati; 2004. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085712249.

Council of Science Editors:

KAKKAR S. METHODOLOGY FOR CLUSTERING SPATIO-TEMPORAL DATABASES. [Masters Thesis]. University of Cincinnati; 2004. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085712249


University of Cincinnati

15. CALENDER, CHRISTOPHER R. APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT.

Degree: MS, Engineering : Computer Science, 2004, University of Cincinnati

 There are often times where an application needs to find the N nearest neighbors. Imagine the scene of an accident and someone needs to know… (more)

Subjects/Keywords: Computer Science; nearest neighbor; cluster; K-means; clustering; approximate nearest neighbor; approximate k-means

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APA (6th Edition):

CALENDER, C. R. (2004). APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952

Chicago Manual of Style (16th Edition):

CALENDER, CHRISTOPHER R. “APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT.” 2004. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952.

MLA Handbook (7th Edition):

CALENDER, CHRISTOPHER R. “APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT.” 2004. Web. 16 Jun 2019.

Vancouver:

CALENDER CR. APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT. [Internet] [Masters thesis]. University of Cincinnati; 2004. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952.

Council of Science Editors:

CALENDER CR. APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT. [Masters Thesis]. University of Cincinnati; 2004. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952


University of Cincinnati

16. ANDRA, SHESHU KALYAN Chakravarthy. TRACKING PHENOMENA WITH SENSOR NETWORKS.

Degree: MS, Engineering : Computer Science, 2005, University of Cincinnati

 The recent emergence of the sensor networks technology is significantly impacting the capabilities for automated distributed monitoring of environments. The large deployments of sensors for… (more)

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APA (6th Edition):

ANDRA, S. K. C. (2005). TRACKING PHENOMENA WITH SENSOR NETWORKS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1109147670

Chicago Manual of Style (16th Edition):

ANDRA, SHESHU KALYAN Chakravarthy. “TRACKING PHENOMENA WITH SENSOR NETWORKS.” 2005. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1109147670.

MLA Handbook (7th Edition):

ANDRA, SHESHU KALYAN Chakravarthy. “TRACKING PHENOMENA WITH SENSOR NETWORKS.” 2005. Web. 16 Jun 2019.

Vancouver:

ANDRA SKC. TRACKING PHENOMENA WITH SENSOR NETWORKS. [Internet] [Masters thesis]. University of Cincinnati; 2005. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1109147670.

Council of Science Editors:

ANDRA SKC. TRACKING PHENOMENA WITH SENSOR NETWORKS. [Masters Thesis]. University of Cincinnati; 2005. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1109147670


University of Cincinnati

17. DESAI, PRANAY A. SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS.

Degree: MS, Engineering : Computer Science, 2005, University of Cincinnati

 The field of Bio-Informatics is fast growing with research in various related topics. One such topic is protein sequence classification. This thesis uses this topic… (more)

Subjects/Keywords: HMM; Hidden Markov Models; Sequence Classification; Viterbi Algorithm; Forward Algorithm; Backward Algorithm

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APA (6th Edition):

DESAI, P. A. (2005). SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500

Chicago Manual of Style (16th Edition):

DESAI, PRANAY A. “SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS.” 2005. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500.

MLA Handbook (7th Edition):

DESAI, PRANAY A. “SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS.” 2005. Web. 16 Jun 2019.

Vancouver:

DESAI PA. SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS. [Internet] [Masters thesis]. University of Cincinnati; 2005. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500.

Council of Science Editors:

DESAI PA. SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS. [Masters Thesis]. University of Cincinnati; 2005. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500


University of Cincinnati

18. KINSEY, MICHAEL LOY. PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES.

Degree: MS, Engineering : Computer Science, 2005, University of Cincinnati

 When applying contemporary decision tree construction techniques, such as the commonly used ID3 algorithm, to situational input stored in geographically distributed databases, several problems can… (more)

Subjects/Keywords: Computer Science; decision tree; privacy preserving; data mining

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APA (6th Edition):

KINSEY, M. L. (2005). PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448

Chicago Manual of Style (16th Edition):

KINSEY, MICHAEL LOY. “PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES.” 2005. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448.

MLA Handbook (7th Edition):

KINSEY, MICHAEL LOY. “PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES.” 2005. Web. 16 Jun 2019.

Vancouver:

KINSEY ML. PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES. [Internet] [Masters thesis]. University of Cincinnati; 2005. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448.

Council of Science Editors:

KINSEY ML. PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES. [Masters Thesis]. University of Cincinnati; 2005. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448


University of Cincinnati

19. ASHOK, RAMYA. A DATABASE SYSTEM TO STORE AND RETRIEVE A CONCEPT LATTICE STRUCTURE.

Degree: MS, Engineering : Computer Science, 2005, University of Cincinnati

 Data mining techniques are utilized to discover noteworthy and unrecognized associations between data items. Organization of discovered concepts in the form of a lattice-structure has… (more)

Subjects/Keywords: Engineering, Mining; Concepts; Hierarchy; Dataset; Lattices; Lattice Structure; Database; Data Mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

ASHOK, R. (2005). A DATABASE SYSTEM TO STORE AND RETRIEVE A CONCEPT LATTICE STRUCTURE. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1130552767

Chicago Manual of Style (16th Edition):

ASHOK, RAMYA. “A DATABASE SYSTEM TO STORE AND RETRIEVE A CONCEPT LATTICE STRUCTURE.” 2005. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1130552767.

MLA Handbook (7th Edition):

ASHOK, RAMYA. “A DATABASE SYSTEM TO STORE AND RETRIEVE A CONCEPT LATTICE STRUCTURE.” 2005. Web. 16 Jun 2019.

Vancouver:

ASHOK R. A DATABASE SYSTEM TO STORE AND RETRIEVE A CONCEPT LATTICE STRUCTURE. [Internet] [Masters thesis]. University of Cincinnati; 2005. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1130552767.

Council of Science Editors:

ASHOK R. A DATABASE SYSTEM TO STORE AND RETRIEVE A CONCEPT LATTICE STRUCTURE. [Masters Thesis]. University of Cincinnati; 2005. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1130552767


University of Cincinnati

20. Muthukrishnan, Arvind Kumar. Information Retrieval Using Concept Lattices.

Degree: MS, Engineering : Computer Science, 2006, University of Cincinnati

 Information retrieval concerns the problem of extracting useful and relevant information. There are many general and domain specific search engines. Most of these systems use… (more)

Subjects/Keywords: Artificial Intelligence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Muthukrishnan, A. K. (2006). Information Retrieval Using Concept Lattices. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141055777

Chicago Manual of Style (16th Edition):

Muthukrishnan, Arvind Kumar. “Information Retrieval Using Concept Lattices.” 2006. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141055777.

MLA Handbook (7th Edition):

Muthukrishnan, Arvind Kumar. “Information Retrieval Using Concept Lattices.” 2006. Web. 16 Jun 2019.

Vancouver:

Muthukrishnan AK. Information Retrieval Using Concept Lattices. [Internet] [Masters thesis]. University of Cincinnati; 2006. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141055777.

Council of Science Editors:

Muthukrishnan AK. Information Retrieval Using Concept Lattices. [Masters Thesis]. University of Cincinnati; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141055777


University of Cincinnati

21. TATAVARTY, GIRIDHAR. FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES.

Degree: MS, Engineering : Computer Science, 2006, University of Cincinnati

 Multivariate time series datasets represents multiple attributes recorded over a period of time. Each attribute may have repeating patterns and there may also be correlations… (more)

Subjects/Keywords: Computer Science; multivariate; time series; data mining; temporal association rules; suffix trees; summarization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

TATAVARTY, G. (2006). FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141325950

Chicago Manual of Style (16th Edition):

TATAVARTY, GIRIDHAR. “FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES.” 2006. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141325950.

MLA Handbook (7th Edition):

TATAVARTY, GIRIDHAR. “FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES.” 2006. Web. 16 Jun 2019.

Vancouver:

TATAVARTY G. FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES. [Internet] [Masters thesis]. University of Cincinnati; 2006. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141325950.

Council of Science Editors:

TATAVARTY G. FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES. [Masters Thesis]. University of Cincinnati; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141325950

.