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Research Of Customers Clustering Based On Improving CABOSFV Algorithm In CRM

Posted on:2005-08-18Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y SongFull Text:PDF
GTID:1116360125470678Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
In the commercial epochs where there is keen competition, possessing resource is fatal to decide enterprises livings. For customer relationship, enterprises always wish to build the most steady relationship and transfer it into profit effectively. It also can be said that keep formal customers, developing new customers and locking those very important customers. This is what CRM want to study mainly. For realizing this goal, enterprises need do their best to know customers behaviors. However, they can' t meet with customers one by one to get information. What they can do is that they do their best to collect information and then discover the inner knowledge and rules from disordered and surface information by kinds of analysis methods, which also can be named Data Ming. After mining mass information, enterprises can design mathematical models according to these rules or information and forecast the unknown results to provide basis for selling decision and market scheme to exhibit CRM.In CRM applied system, data mining system is embedded in and data mining is used to seek implications, unknown before and valuable knowledge and rules benefit to enterprises decision from mass data related to customers. This theme put forward to a kind of customers clustering algorithm based on improving CABOSFV algorithm to solve how to cluster mass high-dimension square data which express customers behaviors after studying CRM and clustering Data Mining technology. There are many dimensions in a database or data warehouse. Some algorithms are good for dealing with few dimensions such as two-dimension or three-dimension data. People can distinguish the qualities of few-dimension data clustering results easily, but the results of many-dimension are not so visual. So, clustering analysisof high-dimension data is challenging. Especially, the distribution of data in high-dimension is very square and not irregular. Thus, data mining technology of clustering high-dimension square data is studied. First, the algorithm of high-dimension square data is mainly studied. Putting up definition of set-difference-degree and calculating formula of threshold to improve the algorithm of CABOSFV. Second, because clustering objects are usually in mass data set, data-reduce-strategy in mass set is studied. How to use sample to reduce data is stated and match non-sample to basis clusters using the conceptions of set upper and lower bound. Third, for perfecting clustering utilities more, mining anomalous data is studied and solution of isolated objects is put forward to make up the probabilities-fault of samples-cluster. Last, the boundary of data mining technology based on clustering analysis using in analytical CRM applied system is studied and transferring algorithm of square feature based on customers purchasing abilities is put forward, then rebuild clustering procedure. Selling medicine of some pharmaceutical factory is used to check and analyze the new procedure.
Keywords/Search Tags:Clustering, Data Mining, Customer Relationship Management, CABOSFV Algorithm
PDF Full Text Request
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