Information Retrieval: Algorithms and Heuristics (The Kluwer International Series on Information Retrieval)
Using text mining and natural language processing for health care claims processing
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Text mining for product attribute extraction
ACM SIGKDD Explorations Newsletter
Text mining for insurance claim cost prediction
Data Mining
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Evaluation of biometric systems: a study of users' acceptance and satisfaction
International Journal of Biometrics
Hi-index | 0.00 |
Spreadsheets applications allow data to be stored with low development overheads, but also with low data quality. Reporting on data from such sources is difficult using traditional techniques. This case study uses text data mining techniques to analyse 12 years of data from dam pump station maintenance logs stored as free text in a spreadsheet application. The goal was to classify the data as scheduled maintenance or unscheduled repair jobs. Data preparation steps required to transform the data into a format appropriate for text data mining are discussed. The data is then mined by calculating term weights to which clustering techniques are applied. Clustering identified some groups that contained relatively homogeneous types of jobs. Training a classification model to learn the cluster groups allowed those jobs to be identified in unseen data. Yet clustering did not provide a clear overall distinction between scheduled and unscheduled jobs. With some manual analysis to code a target variable for a subset of the data, classification models were trained to predict the target variable based on text features. This was achieved with a moderate level of accuracy.