Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a multistage computational procedure for partitioning of large data sets and for segmentation of images. In the first step the original “raw” data set (or the set of pixels from a given image) is compressed by use of the Neural-Gas unsupervised learning algorithm into Compressed Information Model (CIM) that contains small predefined number of neurons. In the second step a graph structure is generated by using all the neurons as nodes of the graph and a number of consistent arcs. Two kinds of consistent arcs are defined here, namely crisp and fuzzy arcs that lead to the respective crisp and fuzzy graph structures. The crisp graphs use the Euclidean distance beten the nodes as “arc lengths”. The fuzzy graphs use ighted arcs with different “arc strengths”, computed by using the ights of the respective adjacent neurons. The third step identifies the number of the strongly connected elements (called also “connected areas”) in the generated graph structure from the previous step. This is done by using the ll known depth-first graph algorithm. Then each connected area corresponds to a respective segment of the given data or image. The proposed computational scheme and its application are demonstrated and explained by two test examples consisting of process data and an image.
['Gancho Vachkov', 'Hidenori Ishihara']
I am writing a research paper on a idea Data Partitioning and Image Segmentation by Use of Information Compression and Graph Structures
[{"from": "human", "value": "I am writing a research paper on a idea Data Partitioning and Image Segmentation by Use of Information Compression and Graph Structures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a multistage computational procedure for partitioning of large data sets and for segmentation of images. In the first step the original \u201craw\u201d data set (or the set of pixels from a given image) is compressed by use of the Neural-Gas unsupervised learning algorithm into Compressed Information Model (CIM) that contains small predefined number of neurons. In the second step a graph structure is generated by using all the neurons as nodes of the graph and a number of consistent arcs. Two kinds of consistent arcs are defined here, namely crisp and fuzzy arcs that lead to the respective crisp and fuzzy graph structures. The crisp graphs use the Euclidean distance beten the nodes as \u201carc lengths\u201d. The fuzzy graphs use ighted arcs with different \u201carc strengths\u201d, computed by using the ights of the respective adjacent neurons. The third step identifies the number of the strongly connected elements (called also \u201cconnected areas\u201d) in the generated graph structure from the previous step. This is done by using the ll known depth-first graph algorithm. Then each connected area corresponds to a respective segment of the given data or image. The proposed computational scheme and its application are demonstrated and explained by two test examples consisting of process data and an image."}]