A Different Approach for Clique and Household Analysis in Synthetic Telecom Data Using Propositional Logic
| Autor: | Kristina Sekrst, Marko Kardum, Sandro Skansi |
|---|---|
| Přispěvatelé: | Koričić, Marko et al. |
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Clique Computer science business.industry 02 engineering and technology Predictive analytics Solver Propositional calculus 01 natural sciences Satisfiability Simple (abstract algebra) Encoding (memory) 0103 physical sciences DPLL algorithm 0202 electrical engineering electronic engineering information engineering Clique Detection SAT Solving DPLL Household Identification SAT encodings Telecom Data Analysis 020201 artificial intelligence & image processing Telecommunications business |
| Zdroj: | MIPRO |
| Popis: | Summary – In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain tasks, it may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding. |
| Databáze: | OpenAIRE |
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