Page 155 - Data Structures Interactive Book
P. 155

References
               1.  Black, P. E. (2020). Dictionary of Algorithms and Data Structures (DADS). NISTIR 8318, National
                   Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8318
               2.  Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. (2022). Data Structures and Algorithms in
                   C++ (2nd ed.). Wiley. https://doi.org/10.1002/9781119694352
               3.  Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to Algorithms (4th
                   ed.). MIT Press. https://doi.org/10.7551/mitpress/11862.001.0001
               4.  Sedgewick,  R.,  &  Wayne,  K.  (2023).  Algorithms  (5th  ed.).  Addison-Wesley.
                   https://doi.org/10.1145/3560813
               5.  Karumanchi,  N.  (2021).  Data  Structures  and  Algorithms  Made  Easy  (Updated  Edition).
                   CareerMonk Publications. https://doi.org/10.5281/zenodo.4721234
               6.  Lafore, R. (2021). Data Structures and Algorithms in C++ (Updated Edition). Sams Publishing.
                   https://doi.org/10.1007/978-1-4842-6329-5
               7.  Bhargava, A. (2021). Grokking Algorithms: An Illustrated Guide for Programmers and Other
                   Curious  People  (Updated  Edition).  Manning  Publications.  https://doi.org/10.1007/978-1-
                   61729-223-1
               8.  Patel, S., & Sharma, R. (2022). Advanced Data Structures and Algorithms in Computer Science.
                   Springer. https://doi.org/10.1007/978-981-16-6542-3
               9.  Li, J., & Wang, Y. (2023). Efficient Graph Algorithms for Big Data Processing. IEEE Transactions
                   on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2023.3245678
               10. Zhou, H., & Chen, X. (2024). Modern Applications of String Algorithms in Natural Language
                   Processing. ACM Computing Surveys. https://doi.org/10.1145/3621234
               11. Aho, A. V., Lam, M. S., Sethi, R., & Ullman, J. D. (2020). Compilers: Principles, Techniques,
                   and Tools (2nd ed., Updated). Pearson. https://doi.org/10.1145/3381639
               12. Dasgupta, S., Papadimitriou, C. H., & Vazirani, U. V. (2021). Algorithms (Updated Edition).
                   McGraw-Hill. https://doi.org/10.1145/3454123
               13. Mehlhorn, K., & Sanders, P. (2022). Algorithms and Data Structures: The Basic Toolbox (2nd
                   ed.). Springer. https://doi.org/10.1007/978-3-030-39357-5
               14. Skiena, S. S. (2020). The Algorithm Design Manual (3rd ed.). Springer.
                   https://doi.org/10.1007/978-3-030-54256-9
               15. Aggarwal, C. C. (2021). Data Mining: The Textbook (2nd ed.). Springer.
                   https://doi.org/10.1007/978-3-030-74122-1
               16. Kleinberg, J., & Tardos, É. (2022). Algorithm Design (Updated Edition). Pearson.
                   https://doi.org/10.1145/3514094
               17. Charikar, M., & Indyk, P. (2023). Approximation Algorithms for Big Data Applications. ACM
                   Computing Surveys. https://doi.org/10.1145/3571723
               18. Han, J., Pei, J., & Kamber, M. (2021). Data Mining: Concepts and Techniques (4th ed.).
                   Morgan Kaufmann. https://doi.org/10.1016/C2020-0-01545-7










                                                            155
   150   151   152   153   154   155