It is suggested that applying association mining techniques can further improve the dealing of information overload in a web oriented retailing environment.
Abstract:
— With the fast development of information technology, retailers are suffering from the excess of information. Too much information can be a problem. However, more information creates more opportunity. In retailing, information is the key issue to maximizing revenue. It is now hard to make timely or effective decisions and to the right content to the right place, at the right time and in the right fo rm. This paper is about managing the information so that the user can gain more clear insight. It is about integrating and inventing methods and techniques. The Semantic Web will provide a foundation for such a solution. However, semantics only provide a way of mapping the content of a web to user defined annotations. Not many companies have fully utilized the power of Internet reta iling due to the various technical obstacles have yet to be overcome. The existing research in e-retailing focuses only on the traditional retailing including direct and indirect retailing approaches. This paper suggests that applying association mining techniques can further improve the dealing of information overload in a web oriented retailing environment.
TL;DR: The World-Wide Web becomes a large directed graph whose vertices are documents and whose edges are links that point from one document to another, which determines the web's connectivity and consequently how effectively the authors can locate information on it.
TL;DR: This paper outlines a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions, and considers, a more formal approach.
TL;DR: Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications as mentioned in this paper, where preprocessing, pattern discovery, and pattern analysis are described in detail.
This paper is about managing the information so that the user can gain more clear insight. The Semantic Web will provide a foundation for such a solution. This paper suggests that applying association mining techniques can further improve the dealing of information overload in a web oriented retailing environment.
Q2. What is the main part of the DCM algorithm?
The pattern matching part, which focus on matching patterns of graphs, like optional parts, union of patterns, nesting, The solution modifiers part , which allows to modify values applying classical operators like projection, distinct, order, limit, and offset.
Q3. What is the use of support in the pruning?
The support is used as the threshold to eliminate unsatisfactory dynamic sets and to obtain the rules from the satisfactory sets.
Q4. What is the polarity of the new data set?
Let Dnew be a new data set constructed from D. A generalized dynamic association rule is an implication of the form A1 →p A2, where A1 ⊂ D, A1 ⊂ D, A1∩ A2= φ and p is the polarity.
Q5. What is the purpose of the ontology?
It is designed to store and retrieve identities that are constructed from triplex collections of strings (sequences of letters) and can be queried with Sparql.
Q6. What are the common tools for developing ontology?
The most common tools for developing ontology are Protégé-2000 (Protege, 2000), Ontolingua (Ontolingua, 1997), and Chimaera (Chimaera, 2000) as ontology-editing environments.
Q7. What is the goal of the semantic network interface?
And the interface should be able to update the ontologies for improvement, thus rather storing a large amount of information, the relevant ontologies or relations are stored.
Q8. What is the main goal of the DCM process?
It also represents a filtering process that prunes away static attributes, which reduces the size of the data set for further mining.
Q9. What is the definition of the single attribute support?
In this case, the single attribute support is defined to be 0.5, which means that if an attribute with polarity +, -, or 0 occurs in more than half of total time stamps, it will be pruned.
Q10. What is the definition of the Web?
The World Wide Web has evolved into a dynamic, distributed, heterogeneous, complex network, which is hard toIcontrol [Albert et al., 99, Huberman & Adamic, 99].
Q11. What is the purpose of system dynamics?
System dynamics [Sterman 1994 & Coyle, 1996], is a tool to visualize and understand such patterns of dynamic complexity, which is build up from a set of system archetypes based on principles in System thinking [Sterman, 2000].
Q12. What is the main idea of the paper?
This paper has considered the most fundamental ways to tackle the problems caused by information overload and complexity in retailing.
Q13. What is the polarity count of the dynamic sets?
While making a pass, one dynamic set is read at a time and the polarity count of candidates supported by the dynamic sets is incremented.
Q14. What is the meaning of the rule plot?
where association rule generation techniques find surface associations, causal inference algorithms identify the structure underlying such associations.