Today , alarm system is an important part of the industry and processes . By using advanced technologies , defining alarms in each section of the process is both easy and cost-effective . Thus , the increased number of alarms in any process may cause confusion for the operator and deviate his/her judgement to identify the root cause of the problem . So improving alarm management systems is very crucial in today's industrial processes . Analysing historical alarm data can identify problems in alarm systems . Data mining algorithms and techniques are utilized to analyze alarm data . Data mining is the science of finding patterns in large data . Various frameworks are available for data mining . One of the most important steps in data mining is the preprocessing of data . Therefore , by considering both data approaches (point-based and interval-based) , a structure is proposed for preprocessing of alarm data . Removing chattering and fleeting alarms and imputing missing alarm messages are the most important parts of preprocessing of alarm data . Several algorithms are introduced to remove chattering alarms and the effects of process variable types are investigated . Then , we have described the concept of missing messages in the interval-based alarms and two methods for imputation are presented . ON-OFF RLD median index and temporal patterns are used to reconstruct missing alarms and then place them into the dataset . The first one is based on the information of the same unique alarms in the dataset , and the latter , on the information of the other unique alarms for estimating missing message time . The goal of the modeling step is to find the related alarms and their relationship as temporal patterns . By using the developed TPMiner algorithm , a new method to find suffix sequences and alarm domain concepts , the ATPMiner algorithm is introduced . The occurrence and duration probability of temporal patterns , provide more information to the analyst about the existing alarms in the pattern . By adopting the developed P-TPMiner algorithm , for calculating the probability of occurrence and the duration of a specific pattern , a new algorithm , AP-TPMiner is introduced . Finally , a graphical user interface is designed based on this algorithm to display the association of the existing alarms in the temporal pattern and related probabilities . This user interface is provided as an analytical tool for the expert analyst . A case study demonstrates the effectiveness of the proposed preprocessing and modeling methods on real industrial alarm data .