I believe so. For example,

- Each node of a binary tree would use 4 array cells: one for a pointer to the father node, two pointers to the two sons, and one for the value.
- Each element of a hash table would use 4 or 5 array cells: the index, possibly a random key that efficiently encode the index, the value, a pointer to the previous index, and a pointer to the next index. This would make finding, updating, deleting or inserting an element a bit slow, but performance can be boosted by storing, at the beginning of the array, a list of pointers to 1,000 indices evenly spaced in the index universe.

A similar arguments can be used for graphs (as in graph theory), non-binary trees, heaps, stacks, linked lists etc.

Indeed, before programming languages offered advanced data structures, sophisticated objects and types, recursion (and much more) -- all the data -- had to be stored in (organized) arrays. Trees, hash tables etc. were simulated by using arrays and pointers. Even recursion.

Are there exceptions? In some ways, we are getting back to the old times, with unstructured data, such as member postings on social networks. Although structuring unstructured data (by putting it into clusters and taxonomies) allow it to be manipulated much more easily.

Tags:

© 2020 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions