Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2000-03-09
2003-01-21
Amsbury, Wayne (Department: 2171)
Data processing: database and file management or data structures
Database design
Data structure types
Reexamination Certificate
active
06510436
ABSTRACT:
BACKGROUND
1. Field of the Invention
The present invention relates to information processing technology. More particularly, the present invention relates to a system and method for clustering large lists of information into optimal segments for human-computer interfaces using fuzzy logic.
2. Description of the Related Art
In client-server applications, the end user is often presented with lists of items from which to make choices or selections. Presenting an end user with a list of items from which to select introduces fewer errors into the system than relying on the end user to enter the information manually using the keyboard. However, allowing the user to select from a list of items becomes untenable when the list of items from which the user is selecting becomes extremely large. The point at which the list is untenable depends on various factors including the size of the user's display screen and the speed at which the data can be transmitted from the server to the client's workstation. For example, a list of 30 items may be very manageable on a 19″ display with 1024×768 pixel resolution, but that same list is untenable on a 14″ display with only 320×200 pixel resolution.
In the field of systems and network management, it is common to have lists of items consisting of thousands or tens of thousands of items. Traditional techniques for clustering these long lists rely on some characteristic or attribute of the item being displayed that can also be used for building the clustered list. For example, a long list of names could be clustered by their DNS hierarchy. A challenge arises, however, when the list is not evenly distributed across the characteristic or attribute used for clustering. Another challenge of the traditional method is that it often requires the end user to understand more information about the item that he or she wishes to select than is desirable.
SUMMARY
It has been discovered that large lists of items can be grouped into clusters so that the items in a given cluster are a manageable size and the affinity between the last item of one cluster and the first item of the second cluster is reduced and the affinity of items within a cluster is accordingly maximized. The client may differ in terms of its display size and its connectivity to the server. Based on these factors, an optimal list size is determined that can optimally be sent to the client computer and displayed on the client's display device. In addition, a maximum manageable list size is determined to provide a maximum list size that can reasonably be handled by the client given the client's connectivity to the server and the client's display size.
The clustering of list data is performed by determining several “fuzzy” scores. These fuzzy scores include the affinity of list items to one another, whether the list size is about equal to the optimal size that can be handled by the client, and whether the list size is less than or about equal to the maximum list size that can be handled by the client. A total score is determined using the aforementioned fuzzy scores. The optimal cluster size is determined by first selecting the cluster based upon the item that receives the highest total score followed by performing an affinity test to determine if the cluster size should be modified due to data list items having a greater affinity score than the affinity score for the item first selected based upon total score.
The foregoing is a summary and thus contains, by necessity, simplifications, generalizations, and omissions of detail; consequently, those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the present invention, as defined solely by the claims, will become apparent in the non-limiting detailed description set forth below.
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Al-Hashemi Sana
Amsbury Wayne
Leeuwen Joseph T. Van
Leeuwen Leslie A. Van
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