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Clustering Is A Useful Technique That Organizes A Large Quantity Of Unordered Text Documents Into A Small Number Of Meaningful And Coherent Cluster, Thereby Providing A Basis For Intuitive And Informative Navigation And Browsing Mechanisms. There Are Some Clustering Methods Which Have To Assume Some Cluster Relationship Among The Data Objects That They Are Applied On. Similarity Between A Pair Of Objects Can Be Defined Either Explicitly Or Implicitly. The Major Difference Between A Traditional Dissimilarity/similarity Measure And Ours Is That The Former Uses Only A Only A Single Viewpoint, Which Is The Origin, While The Latter Utilizes Many Different Viewpoints, Which Are Objects Assumed To Not Be In The Same Cluster With The Two Objects Being Measured. Using Multiple Viewpoints, More Informative Assessment Of Similarity Could Be Achieved. Theoretical Analysis And Empirical Study Are Conducted To Support This Claim. Two Criterion Functions For Document Clustering Are Proposed Based On This New Measure. We Compare Them With Several Well-known Clustering Algorithms That Use Other Popular Similarity Measures On Various Document Collections To Verify The Advantages Of Our Proposal