A clustering coefficient-based adaptive clustering method, according to the categories of extracted data point pairs and the magnitude of association relations between data points extracted each time, determining to which category the data points belong, and the number of the categories, and establishing association relations between the data points and association relations between the categories; pre-segmenting each category and calculating the intra-category similarities of two sub-categories and inter-category similarities of the two sub-categories, judging whether the two pre-segmented sub-categories satisfy a segmentation condition, if so, then accepting the pre-segmentation; if not, then canceling the pre-segmentation; calculating the intra-category similarities and inter-category similarities of two categories having an association relation, judging whether the two categories satisfy a merging condition, if so, then merging the two categories to generate a new category; if not, then abandoning the merging.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A clustering coefficient-based adaptive clustering method, used for a set of elements to be clustered in a computer network system constructed by a plurality of servers and clients, the set comprising data points in a plurality of clustering object sets, and characterized in that the clustering coefficient-based adaptive clustering method comprises: a preliminary clustering step, i.e., acquiring association relations between the data points in the plurality of clustering object sets, acquiring the number of neighbor points in a nearest-neighbor algorithm applicable for searching of the number of the neighbor points, and acquiring a distribution curve convergence rate of the data points through a curve fitting technique; sequentially extracting data point pairs according to a magnitude of the association relations between the data points, determining to which category the data points belongs and the number of the categories according to the category of the extracted data point pairs and the magnitude of the association relation between the data points extracted each time, and establishing a association relation between the data points and a association relation between the categories till all data points are processed to acquire preliminary clustering results; and a self-learning clustering step, comprising: a segmentation step, i.e., calculating an intra-category similarity of each category, calculating a local clustering coefficient of data points in each category, comparing the intra-category similarity of each category with the local clustering coefficient of the data points in each category, performing pre-segmentation according to a segmentation mode of segmenting each category into two sub-categories which respectively have a local clustering coefficient of data points greater than or equal to the intra-category similarity and a local clustering coefficient of data points smaller than the intra-category similarity, calculating the intra-category similarities of the two sub-categories and the inter-category similarities of the two sub-categories, judging whether the two pre-segmented sub-categories satisfy a segmentation condition, if so, then accepting the pre-segmentation, generating a category set consisting of a plurality of categories and continuously performing a next step; if not, then canceling the pre-segmentation; a searching step, i.e., searching for two categories having a association relation in the category set; and a merging step, i.e., calculating the intra-category similarities of the two categories having the association relation and the inter-category similarities of the two categories, judging whether the two categories having the association relation satisfy a merging condition, if so, indicating the relation between the two categories is close, and then merging the two categories to generate a new category; if not, indicating the relation between the two categories is distant, and then abandoning the merging. 2. The clustering coefficient-based adaptive clustering method according to claim 1, characterized in that the preliminary clustering step in the clustering coefficient-based adaptive clustering method further comprises the steps of: performing statistics to distribution information of the association relations between the data points in the plurality of clustering object sets, and acquiring a curve convergence rate of the association relations between the data points in the plurality of clustering object sets by adopting the curve fitting technique according to the statistical distribution information; and performing statistics to the distribution information of the number of neighbor points between the data points according to the nearest-neighbor algorithm for searching the number of the neighbor points, acquiring a determined value of the number of the neighbor points, judging whether the number of the data points having the number of neighbor points greater than the determined value of the number of the neighbor points is greater than a predetermined number, if so, then continuously executing the step of sequentially extracting the data point pairs according to the magnitude of the association relation between the data points; if not, terminating the process. 3. The clustering coefficient-based adaptive clustering method according to claim 1, characterized in that: the segmentation step further comprises steps of updating the inter-category association relations between the two sub-categories and other categories, and deleting the inter-category association relations between the category to which the two sub-categories originally belongs and other categories; the merging step further comprises steps of updating the inter-category association relations between a new generated category and other categories, and deleting the inter-category association relations between the two categories and other categories. 4. The clustering coefficient-based adaptive clustering method according to claim 1, characterized in that the two sub-categories in the segmentation step comprise a sub-category A and a sub-category B, formulas for calculating the intra-category similarities of the two sub-categories A and B are respectively as follows: clu  ( A ) = ∑ v i ∈ V A , e ij ∈ E A  C  ( i )  V A   1  ; clu  ( B ) = ∑ v i ∈ V B , e ij ∈ E B  C  ( i )  V B  ; wherein Vi denotes data point i, VA denotes a set of data points in the sub-category A, VB denotes a set of data points in the sub-category B, |VA| denotes the number of data points in the sub-category A, |VB| denotes the number of data points in the sub-category B, eij denotes the edge connecting the data point i and the data point j, EA denotes a set of edges in the sub-category A, EB denotes a set of edges in the sub-category B, and C(i) is a local clustering coefficient of the data point Vi, C  ( i ) =  { e jk  :   V j , V k ∈ N  ( i ) , e jk ∈ E }  k i  ( k i - 1 ) 2  ∑ e jk ∈ N  ( i )  e jk k i , wherein Vj, Vk denote data points j, k, N(i) denotes a neighbor point set of the data point Vi, E is a set of edges, k, denotes the number of N(i), and ejk denotes the edge connecting data point j and data point k; and formulas for calculating the inter-category similarities of the two sub-categories A and B are respectively as follows: assoc  ( A ′ ) = ∑ v i ∈ V A ′  e ij ∈ U  C  ( i )  V A ′  ; assoc  ( B ′ ) = ∑ v i ∈ V B ′  e ij ∈ U  C  ( i )  V B ′  ; wherein Vi denotes the data point i, A′ denotes the sub-category A and the category of data points which belongs to the category B and has association relations with the sub-category A, B′ denotes the sub-category B and the category of data points which belongs to the sub-category A and has association relations with the sub-category B, |VA′| denotes the number of data points in category A′, |VB′| denotes the number of data points in category B′, VA denotes a set of data points in the sub-category A, VB denotes a set of data points in the sub-category B, eij denotes the edge connecting the data point i and the data point j, and C(i) is a local clustering coefficient of the data point Vi, C  ( i ) =  { e jk  :   V j , V k ∈ N  ( i ) , e jk ∈ E }  k i  ( k i - 1 ) 2  ∑ e jk ∈ N  ( i )  e jk k i . 5. The clustering coefficient-based adaptive clustering method according to claim 4, characterized in that the segmentation condition is that the intra-category similarities of the two sub-categories are respectively greater than the inter-category similarities of the two sub-categories, i.e., clu(A)>assoc(A′) and clu(B)>assoc(B′). 6. The clustering coefficient-based adaptive clustering method according to claim 1, characterized in that, in the merging step, the two categories comprise a category A1 and a category A2, formulas for calculating the intra-category similarities of the two categories A1 and A2 are respectively as follows: clu  ( A   1 ) = ∑ v i ∈ V A   1 , e ij ∈ E A   1  C  ( i )  V A   1  ; clu  ( A   2 ) = ∑ v i ∈ V A   2 , e ij ∈ E A   1  C  ( i )  V A   2  ; wherein Vi denotes a data point i, VA1 denotes a set of data points in the category A1, VA2 denotes a set of data points in the category A2, |VA1| denotes the number of the data points in the category A1, |VA2| denotes the number of the data points in the category A2, eij denotes the edge connecting the data point i and the data point j, EA1 denotes a set of edges in the category A1, EA2 denotes a set of edges in the category A2, and C(i) is a local clustering coefficient of the data point Vi, C  ( i ) =  { e jk  :   V j , V k ∈ N  ( i ) , e jk ∈ E }  k i  ( k i - 1 ) 2  ∑ e jk ∈ N  ( i )  e jk k i , wherein Vj Vk denote the data points j, k, N(i) denotes a set of neighbor points of the data point Vi, E is a set of edges, ki denotes the number of N(i), and ejk denotes the edge connecting the data point j and the data point k; and formulas for calculating the inter-category similarities of the two categories A1 and A2 are respectively as follows: assoc  ( A   1 ′ ) = ∑ v i ∈ V A   1 ′ , e ij ∈ U  C  ( i )  V A   1 ′  ; assoc  ( A   2 ′ ) = ∑ v i ∈ V A   2 ′  e ij ∈ U  C  ( i )  V A   2 ′  ; wherein Vi denotes the data point i, A1′ denotes the category A1 and the category of data points which belong to category A2 and have association relations with the category A1, A2′ denotes the category A2 and the category of data points which belong to category A1 and have association relations with category A2, |VA1′| denotes the number of data points in the category A1′, |VA2′| denotes the number of data points in the category A2′, VA1 denotes a set of data points in the category A1, VA2 denotes a set of data points in the category A2, eij denotes the edge connecting the data point i and the data point j, and C(i) is a local clustering coefficient of the data point Vi, C  ( i ) =  { e jk  :   V j , V k ∈ N  ( i ) , e jk ∈ E }  k i  ( k i - 1 ) 2  ∑ e jk ∈ N  ( i )  e jk k i . 7. The clustering coefficient-based adaptive clustering method according to claim 6, characterized in that the merging conditions are that the inter-category similarities of the two categories are respectively greater than the intra-category similarities of the two categories, i.e., clu(A1)≦assoc(A1′) and clu(A2)≦assoc(A2′). 8. The clustering coefficient-based adaptive clustering method according to claim 1, characterized in that the step of sequentially extracting the data point pairs according to the magnitude of the association relations between the data points further comprises the step of detecting whether the extracted data point pairs satisfy features of one category according to a detection principle. 9. The clustering coefficient-based adaptive clustering method according to claim 8, characterized in that the detection principle is as follows: supposing that the number of data points in the one category is n1, an intra-category average relation weight is avg(n1), a relation weight between the extracted data points and intra-category data points in the one category is w1 and an average relation weight convergence rate of the one category is yn1′, if a formula (1+yn1′)*avg(n1)>w1 is satisfied, the extracted data points can be added into the one category; or else, the extracted data points cannot be added into the one category. 10. A clustering coefficient-based adaptive clustering system, used for a set of elements to be clustered in a computer network system constructed by a plurality of servers and clients, the set comprising data points in a plurality of clustering object sets, and characterized in that the clustering coefficient-based adaptive clustering system comprises: a preliminary clustering module used for completing preliminary clustering, the preliminary clustering module comprising a preprocessing unit and a preliminary clustering unit, wherein the preprocessing unit is used for acquiring association relations between the data points in the plurality of clustering object sets, acquiring the number of neighbor points in a nearest-neighbor algorithm applicable for searching of the number of the neighbor points, and acquiring a distribution curve convergence rate of the data points through a curve fitting technique; the preliminary clustering unit is used for sequentially extracting data point pairs according to magnitude of association relations between the data points, determining to which category the data points belongs and the number of the categories according to the categories of the extracted data point pairs and the magnitude of association relations between the extracted data points, and establishing association relations between the data points and association relations between the categories till all data points are processed to acquire preliminary clustering results; and a self-learning clustering module used for completing self-learning clustering, the self-learning clustering module comprising a first calculation unit, a pre-segmentation unit, a first judgment unit, a searching unit, a second calculation unit, a second judgment unit and a merging unit, wherein the first calculation unit is used for calculating an intra-category similarity of each category, calculating a local clustering coefficient of data points in each category, comparing the intra-category similarity of each category with the local clustering coefficient of the data points in each category, and calling the pre-segmentation unit; the pre-segmentation unit is used for performing pre-segmentation according to a segmentation mode of segmenting each category into two sub-categories which respectively have a local clustering coefficient of data points greater than or equal to the intra-category similarity and a local clustering coefficient of data points smaller than the intra-category similarity, and calculating intra-category similarities of the two sub-categories and inter-category similarities of the two sub-categories; the first judgment unit is used for judging whether the two pre-segmented sub-categories satisfy the segmentation condition, if so, then accepting the pre-segmentation and generating a category set consisting of a plurality of categories; if not, then canceling the pre-segmentation; the searching unit is used for searching two categories having a association relation in the category set; the second calculation unit is used for calculating intra-category similarities of the two categories having the association relation and inter-category similarities of the two categories; and the second judgment unit is used for judging whether the two categories having the association relation satisfy a merging condition, if so, indicating the relation between the two categories is close, and then calling the merge unit to merge the two categories to generate a new category; if not, indicating the relation between the two categories is distant, and then abandoning the merging.