Abstract—We describe a novel application of using data mining and statistical learning methods to automatically monitor and detect abnormal execution traces from console logs in an online setting. Different from existing solutions, we use a two stage detection system. The first stage uses frequent

With the fast increase in Web activities, Web data mining has recently become an important research topic. However, most previous studies of mining path traversal patterns are based on the model of a uniform support threshold without taking into consideration such important factors as the length

number of approaches that combine data warehousing and data mining techniques in order to analyze Web logs. After introducing the well-known click and session data warehouse (DW) schemas, the chapter presents the subsession schema, which allows fast queries on sequences

the task which we used. Aimed to overcome the deficiency of abundant data to web mining, the study proposed a path extraction using Euclidean Distance based algorithm with a sequential pattern clustering mining algorithm. First, we construct the Relational Information System using original data sets

on small benchmark datasets, so their performance on a large scale is hard to predict. This pa-per stresses the importance of data preprocessing and introduces an efficient method for mining patterns in large sized collections of web logs (of all types) based on classic set theory properties.

fast top-down algorithm is presented to discover maximal traversal paths which are contiguous sequences in web log session sequences. The algorithm avoids candidate sequence generation and searches only maximal potential patterns in the minimized search space during mining process. Experimental results

Abstract- We describe a pre-processing technique for mining a telecommunication alarm log for frequent temporal patterns. The method consists in extracting relevant subsets from the initial log with the aim of discovering frequent patterns more accurately. In a first step, the alarm types

text mining, log processing, supervised machine learning, quantification, text classification, applications, pattern recognition We discuss our experiences in analyzing customer-support issues from the unstructured free-text fields of technical-support call logs. The identification of frequent

, artificial intelligence, chart technology and so on to the web data and traces user’s visiting behaviors and extracts their interests using patterns. Because of its direct application in e-commerce, Web analytics, e-learning, information retrieval, web mining has become one of the important areas in computer

Abstract:- Sequential patterns mining is now widely used in many areas, such as the analysis of e-Learning sequential patterns, web log analysis, customer buying behavior analysis and etc. In the discipline of data mining, runtime and search space are always the two major issues. In this paper, we