Automated Clustering of Win32 Applications Based on Failure Behavior
نویسندگان
چکیده
We present a preliminary exploration of application crash events in Win32 systems by automatic clustering. The crash events, collected from workstations in the UC Berkeley electrical engineering and computer science department, identify the crashed application and DLL, and the time of the crash event. We use these identifying features to augment the crash data with information about usage patterns and program dependencies. Preliminary results highlight the importance of identifying features of collected crash data that provide information about program structure, system configurations, and user behaviors, and defining distance measures for clustering that use those features effectively.
منابع مشابه
A Data Mining approach for forecasting failure root causes: A case study in an Automated Teller Machine (ATM) manufacturing company
Based on the findings of Massachusetts Institute of Technology, organizations’ data double every five years. However, the rate of using data is 0.3. Nowadays, data mining tools have greatly facilitated the process of knowledge extraction from a welter of data. This paper presents a hybrid model using data gathered from an ATM manufacturing company. The steps of the research are based on CRISP-D...
متن کاملA Clustering Approach by SSPCO Optimization Algorithm Based on Chaotic Initial Population
Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. SSPCO optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. One of the things that smart algorithms are applied to solve is the problem ...
متن کاملA Joint Semantic Vector Representation Model for Text Clustering and Classification
Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...
متن کاملSolving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization
In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...
متن کاملSolving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization
In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...
متن کامل