William Perrizo
About
In The Last Decade
William Perrizo
79 papers receiving 456 citations
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 262
- Information Systems 175
- Signal Processing 139
- Computer Networks and Communications 128
- Computer Vision and Pattern Recognition 71
Countries citing papers authored by William Perrizo
This map shows the geographic impact of William Perrizo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by William Perrizo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Perrizo more than expected).
Fields of papers citing papers by William Perrizo
This network shows the impact of papers produced by William Perrizo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by William Perrizo. The network helps show where William Perrizo may publish in the future.
Co-authorship network of co-authors of William Perrizo
This figure shows the co-authorship network connecting the top 25 collaborators of William Perrizo. A scholar is included among the top collaborators of William Perrizo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with William Perrizo. William Perrizo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | Experimental Study on Item-based P-Tree Collaborative Filtering Algorithm for Netflix. | 2 |
| 3 | Parameter Optimized, Vertical, Nearest-Neighbor-Vote and Boundary-Based Classification. | 1 |
| 4 | A Hybrid Clustering Method for Gene Expression Data. | 0 |
| 5 | Vertical K-Median Clustering. | 0 |
| 6 | Clustering Microarray Data based on Density and Shared Nearest Neighbor Measure. | 4 |
| 7 | Aggregate Function Computation and Iceberg Querying in Vertical Database. | 3 |
| 8 | Vertical Set Square Distance Based Clustering without Prior Knowledge of K. | 3 |
| 9 | An Api for Transparent Distributed Vertical Data Mining. | 1 |
| 10 | Efficient Ranking of Keyword Queries Using P-trees | 2 |
| 11 | A Distance-based Outlier Detection Method Using P-Tree. | 1 |
| 12 | Multi-Layered Framework for Distributed Data Mining. | 3 |
| 13 | Efficient Quantitative Frequent Pattern Mining Using Predicate Trees. | 1 |
| 14 | Cluster Analysis of Spatial Data Using Peano Count Tree. | 0 |
| 15 | Lazy Classifiers Using P-trees. | 4 |
| 16 | Efficient Hierarchical Clustering of Large Data Sets Using P-trees. | 4 |
| 17 | A New Method for Concurrency Control in Centralized Database Systems. | 3 |
| 18 | Performance Improvement for Bayesian Classification on Spatial Data with P-Trees. | 1 |
| 19 | ANPA - A two-phase commit protocol for distributed databases. | 1 |
| 20 | The Structure of attractors in dynamical systems : proceedings, North Dakota State University, June 20-24, 1977 | 0 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.