Mining gene expression data with pattern structures in formal concept analysis

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

“…If a restriction q is over a nominal attribute a which is materialized by a subset s q ⊆ dom(a) membership, neighbors of q are candidates r which correspond to singletons of s q . Similarly for a numeric attribute, candidate neighbors of a restriction r are the intervals q resulting from a left-minimal change or a right-minimal change on the interval bounds corresponding to r [12]. With these tools, we can easily define a refinement operator η : D → 2 D which maps to each description d its neighbors in D and we have:…”

A Greedy Clustering Algorithm Based on Interval Pattern Concepts and the Problem of Optimal Box Positioning

“…In the case = 2 we arrive at the same 6 clusters. When = 3 we have five clusters { , } ◻ = { [8,9], [9,9], [9,10], [6,9]}, { }, { }, { }, { }, and in the case = 4 we obtain three clusters { , , } ◻ = { [6,9], [5,9], [9,10], [6,9]}, { , } ◻ = { [8,8], [2,4], [6,6], [5,9]}, { }. Example 2.…”

“…In the conditions of the previous example let us set 1 = 1, 2 = 1, 3 = 10, 4 = 3. Then the set of pupils can be divided into four clusters { , }, { , }, { }, { }: [9,9] , [9,10] , [6,9]} , { , } ◻ = { [6,6] , [5,5] , [2,10] , [7,10]} , [4,4] , [6,6] , [9,9]} .…”

Research on the Gene Sequence Data Mining Model Based on Information Theory and Data Dimension Reduction Algorithm

“…Mehdi used pattern structures to undertake gene expression data mining task in formal concept analysis [3]. They compare two FCA-based methods for mining numerical data and show the equivalent features of the two approaches.…”