By Nicolas Werro
This ebook introduces a fuzzy category process, which mixes relational databases with fuzzy good judgment for more advantageous and strong buyer dating administration (CRM). It indicates the advantages of a fuzzy type not like the conventional sharp overview of consumers for the purchase, retention and restoration of consumers in on-line outlets. The booklet starts off with a presentation of the fundamental recommendations, fuzzy set idea and the combo of relational databases and fuzzy class. In its moment half, it specializes in the client point of view, detailing the crucial options of CRM, its theoretical constructs and points of analytical, operational and collaborative CRM. It juxtaposes fuzzy and sharp shopper periods and indicates the consequences for consumer positioning, mass customization, personalization, patron overview and controlling. ultimately, the booklet provides the appliance and implementation of the thoughts in on-line outlets. a close case research offers the appliance and a separate bankruptcy introduces the bushy class question Language (fCQL) toolkit for enforcing those options. In its appendix the ebook lists the bushy set operators and the question language’s grammar.
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Additional resources for Fuzzy Classification of Online Customers
A semantics), the dynamically created classes are not labelled, therefore their name is the concatenation of the names of the linguistic variable and term as well as the chosen connectors, if any. Querying new classes requires a good knowledge of the fuzzy classification definition as the semantics of these classes has to be carefully interpreted to avoid misleading conclusions. 00)} low non attractive C4 Going one step further, a classification query can be formulated using two linguistic variables and terms: classify from with Customer, Turnover, Behavior CustomerTable Turnover is high and Behavior is attractive In fact this query returns the predefined class C1 with semantics ‘Commit Customer’.
This operator has a γ-argument ranging from 0 to 1 which specifies whether the results should go in the direction of the algebraic product (t-norm) or the algebraic sum (t-conorm). The γ-argument therefore determines the strength the compensation mechanism. 21 The γ-operator of m fuzzy sets A1 , . . , Am defined over a reference set U with membership functions μ1 , . . , μm is defined as (1−γ) m μ Ai ,comp (x) = μi (x) i=1 γ m (1 − μi (x)) 1− , γ ∈ [0, 1] and x ∈ U i=1 Another interesting kind of operators are the linguistic modifiers (also called linguistic hedges) which expresses linguistic notions like ‘a little’, ‘slightly’, ‘very’, ‘extremely’, ‘more or less’, etc.
Due to the respect of the normal forms, relational databases suffer from the following restrictions : • Queries to a relational database system are sharp queries. They have a dichotomic character since the criteria value and the tuple component are either identical or not. A retrieval of tuples which are more or less identical is not possible. e. it is not ambiguous or unclear (with exception of null values ). This comes from the requirement of the classical relational data model which only accepts atomic tuple components.