13. The method of claim 1 , wherein the selecting a set of representative keywords comprises: ranking words in the document cluster based on a computing algorithm; and selecting top K words based on the ranking as the representative keywords, wherein K is a predefined number; wherein if constrained information-theoretic co-clustering is used as the constrained clustering algorithm, the computing algorithm comprises, for each word v i in the documents in segment S, computing a rank of the word r v i by r v i ∈ ⁢ S = ∑ k v ⁢ ⁢ q ⁡ ( d ⋒ k d , v ⋒ k v ) ⁢ q ⁡ ( v i ❘ v ⋒ k v ) , wherein {circumflex over (d)} k d is the document cluster associated with segment S, q({circumflex over (d)} k d ,{circumflex over (v)} k v ) is a probability of a word cluster {circumflex over (v)} k v , k v =1, 2, 3 . . . , K, associated with the document cluster {circumflex over (d)} k d , and q(v i |{circumflex over (v)} k v ) is a probability a word v i is associated with the word cluster {circumflex over (v)} k v ; and wherein if constrained co-clustering with non-negative matrix trifactorization is used as the constrained clustering algorithm, the computing algorithm comprises, computing a rank of a word v i by r v i =[S·G 2 T ] k,i , wherein S is a cluster center matrix of a matrix-factorization-based constrained co-clustering objective function, G 2 T is a transpose of a word cluster indicator matrix, k is a cluster label associated with current segment, and i is a word index of v i .
1. A method of visualizing content change of a data collection over time, comprising: receiving one or more topics derived from the data collection; extracting a set of documents relevant to each topic from the data collection; segmenting each topic into multiple non-overlapping temporal segments, the segmenting comprising: generating temporal and visual constraints; deriving from the set of documents a set of document clusters that satisfy the temporal and visual constraints, a document cluster in the set of document clusters identified with a sub-topic; and determining a temporal segment boundary associated with said document cluster, wherein the temporal segment boundary identifies a temporal segment for the sub-topic; selecting a set of representative keywords from the document cluster to summarize the temporal segment; and generating a time-based, interactive visual summary of the data collection.