1. A computer-based method of reclassifying and clustering electronic documents of a document corpus to improve classification of the electronic documents so that correct documents are returned as a result of a computer-based search, the method comprising: comparing, by a computer, each individual electronic document in the document corpus with each other electronic document in the document corpus, thereby forming document pairs, wherein the electronic documents of the document pairs are compared by: calculating a similarity value with respect to the electronic documents of a document pair from a plurality of attributes of the electronic documents in the document corpus, the plurality of attributes comprising a citation attribute, a text-based attribute, and one or more of the following attributes: an author attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ common ⁢ ⁢ authors number ⁢ ⁢ of ⁢ ⁢ distinct ⁢ ⁢ authors , a publication attribute, an institution attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ common ⁢ ⁢ institutions number ⁢ ⁢ of ⁢ ⁢ distinct ⁢ ⁢ institutions , a downloads attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ downloads ⁢ ⁢ of ⁢ ⁢ two documents ⁢ ⁢ in ⁢ ⁢ a ⁢ ⁢ same ⁢ ⁢ time ⁢ ⁢ period total ⁢ ⁢ number ⁢ ⁢ of ⁢ ⁢ downloads ⁢ ⁢ of ⁢ ⁢ two ⁢ ⁢ documents , and a clustering results attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ common ⁢ ⁢ clusters total ⁢ ⁢ number ⁢ ⁢ of ⁢ ⁢ clusters , wherein the calculating comprises: calculating a similarity vector S (p,q) for each document pair in the document corpus, wherein the similarity vector S (p,q) is expressed by: S _ ⁡ ( p , q ) = ( s ⁡ ( p , q ) 1 ⋮ s ⁡ ( p , q ) n ) , where s(p,q) is a similarity measure of a first electronic document p to a second electronic document q of the document pair with respect to an individual attribute of the plurality of attributes; and calculating the similarity value for each document pair comprises summing weighted individual similarity measures of the similarity vector S (p,q) such that the similarity value is expressed by: S ⁡ ( p , q ) = ∑ i ⁢ w i ⁢ s ⁡ ( p , q ) i where w i is a weighting factor for each attribute i of the plurality of attributes; and associating the similarity value with both electronic documents of the document pair; generating a plurality of hierarchical clusters by applying a clustering algorithm to the document corpus using the similarity values; organizing documents into the plurality of hierarchical clusters by saving metadata associated with each individual electronic document in the document corpus and each of the plurality of hierarchical clusters to which each individual electronic document in the document corpus has been assigned such that each individual electronic document is reclassified in a new classification according to the hierarchical cluster; and providing the documents in the plurality of hierarchical clusters such that, when the computer-based search is completed, the computer-based search searches the document corpus and the results are derived from the saved metadata and are graphically presented at a user computing device as a plurality of dots, each dot in the plurality of dots corresponding to each electronic document of the document corpus, the plurality of dots arranged according to the plurality of hierarchical clusters.
1. A computer-based method of reclassifying and clustering electronic documents of a document corpus to improve classification of the electronic documents so that correct documents are returned as a result of a computer-based search, the method comprising: comparing, by a computer, each individual electronic document in the document corpus with each other electronic document in the document corpus, thereby forming document pairs, wherein the electronic documents of the document pairs are compared by: calculating a similarity value with respect to the electronic documents of a document pair from a plurality of attributes of the electronic documents in the document corpus, the plurality of attributes comprising a citation attribute, a text-based attribute, and one or more of the following attributes: an author attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ common ⁢ ⁢ authors number ⁢ ⁢ of ⁢ ⁢ distinct ⁢ ⁢ authors , a publication attribute, an institution attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ common ⁢ ⁢ institutions number ⁢ ⁢ of ⁢ ⁢ distinct ⁢ ⁢ institutions , a downloads attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ downloads ⁢ ⁢ of ⁢ ⁢ two documents ⁢ ⁢ in ⁢ ⁢ a ⁢ ⁢ same ⁢ ⁢ time ⁢ ⁢ period total ⁢ ⁢ number ⁢ ⁢ of ⁢ ⁢ downloads ⁢ ⁢ of ⁢ ⁢ two ⁢ ⁢ documents , and a clustering results attribute expressed as s ⁡ ( p , q ) = number ⁢ ⁢ of ⁢ ⁢ common ⁢ ⁢ clusters total ⁢ ⁢ number ⁢ ⁢ of ⁢ ⁢ clusters , wherein the calculating comprises: calculating a similarity vector S (p,q) for each document pair in the document corpus, wherein the similarity vector S (p,q) is expressed by: S _ ⁡ ( p , q ) = ( s ⁡ ( p , q ) 1 ⋮ s ⁡ ( p , q ) n ) , where s(p,q) is a similarity measure of a first electronic document p to a second electronic document q of the document pair with respect to an individual attribute of the plurality of attributes; and calculating the similarity value for each document pair comprises summing weighted individual similarity measures of the similarity vector S (p,q) such that the similarity value is expressed by: S ⁡ ( p , q ) = ∑ i ⁢ w i ⁢ s ⁡ ( p , q ) i where w i is a weighting factor for each attribute i of the plurality of attributes; and associating the similarity value with both electronic documents of the document pair; generating a plurality of hierarchical clusters by applying a clustering algorithm to the document corpus using the similarity values; organizing documents into the plurality of hierarchical clusters by saving metadata associated with each individual electronic document in the document corpus and each of the plurality of hierarchical clusters to which each individual electronic document in the document corpus has been assigned such that each individual electronic document is reclassified in a new classification according to the hierarchical cluster; and providing the documents in the plurality of hierarchical clusters such that, when the computer-based search is completed, the computer-based search searches the document corpus and the results are derived from the saved metadata and are graphically presented at a user computing device as a plurality of dots, each dot in the plurality of dots corresponding to each electronic document of the document corpus, the plurality of dots arranged according to the plurality of hierarchical clusters. 2. The computer-based method of claim 1 , wherein the citation attribute is based on citations between the electronic documents of the document pair and/or between the other electronic documents in the document corpus, and the text-based attribute is based on keywords extracted from the electronic documents of the document corpus.