Connectionist and evolutionary models for learning, discovering and forecasting software effort

Authors:
Parag C. Pendharkar;Girish Subramanian
Affiliations:
Pennsylvania State University at Harrisburg;Pennsylvania State University at Harrisburg
Venue:
Managing data mining technologies in organizations
Year:
2003

Citing 36
Cited 0

Algorithms for clustering data

Algorithms for clustering data
A general lower bound on the number of examples needed for learning

Information and Computation
Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Symbolic and Neural Learning Algorithms: An Experimental Comparison

Machine Learning
The Use of Background Knowledge in Decision Tree Induction

Machine Learning
Performance comparisons between backpropagation networks and classification trees on three real-world applications

Advances in neural information processing systems 2
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Comparing connectionist and symbolic learning methods

Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Fast subsequence matching in time-series databases

SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A comparative study of classification algorithms: statistical, machine learning and neural network

Machine intelligence 13
BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Bayesian classification (AutoClass): theory and results

Advances in knowledge discovery and data mining
Software failure: management failure: amazing stories and cautionary tales

Software failure: management failure: amazing stories and cautionary tales
A field study of scale economies in software maintenance

Management Science - Special issue: Frontier research on information systems and economics
Using Feature Construction to Improve the Performance of Neural Networks

Management Science
Data Mining: An Overview from a Database Perspective

IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Association Rules in Distributed Databases

IEEE Transactions on Knowledge and Data Engineering
The CN2 Induction Algorithm

Machine Learning
Induction of Decision Trees

Machine Learning
Database Mining: A Performance Perspective

IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases

FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
HierarchyScan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences

ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Interval Classifier for Database Mining Applications

VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification

SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Data mining for path traversal patterns in a web environment

ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
An examination of some software development effort and productivity determinants in ICASE tool projects

Journal of Management Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mining information and knowledge from very large databases is recognized as a key research area in machine learning and expert systems. In the current research, we use connectionist and evolutionary models for learning software effort. Specifically, we use these models to learn the software effort from a set of training data set containing information on software projects and test the performance of the model on a holdout sample. The design issues of developing connectionist and evolutionary models for mining software effort patterns on a data set are described. Our research indicates that connectionist and evolutionary models, whenever carefully designed, hold a great promise for knowledge discovery and forecasting software effort.