Managing Email Overload with an Automatic Nonparametric Clustering Approach
نویسندگان
چکیده
Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.
منابع مشابه
Data Mining in Personal Email Management
E-mail is still a popular mode of Internet communication and contains a large percentage of every-day information. Hence, email overload has grown over the past years becoming a problem for personal information management for users and a financial issue for companies. This survey reviews research on how a Machine Learning and Data Mining technique, such as classification, clustering can contrib...
متن کاملSentence Clustering-based Summarization of Multiple Text Documents
With the rapid growth of the World Wide Web, information overload is becoming a problem for an increasingly large number of people. Automatic Multidocument summarization can be an indispensable solution to reduce the information overload problem on the web. This kind of summarization facility helps users to see at a glance what a collection is about and provides a new way of managing a vast hoa...
متن کاملImproved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring
In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank...
متن کاملFuzzy Clustering Approach Using Data Fusion Theory and its Application To Automatic Isolated Word Recognition
In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...
متن کاملEmail Mining: Emerging Techniques for Email Management
Email has met tremendous popularity over the past few years. People are sending and receiving many messages per day, communicating with partners and friends, or exchanging files and information. Unfortunately, the phenomenon of email overload has grown over the past years becoming a personal headache for users and a financial issue for companies. In this chapter, we will discuss how disciplines...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007