This paper presents a novel approach based on an improved Conserved Self Pattern Recognition Algorithm to analyze cytological characteristics of breast fine-needle aspirates (FNAs) for clinical breast cancer diagnosis. A novel detection strategy by coupling domain knowledge and randomized methods is proposed to resolve conflicts on anomaly detection between two types of detectors investigated in our earlier work on Conserved Self Pattern Recognition Algorithm (CSPRA). The improved CSPRA is applied to detect the malignant cases using clinical breast cancer data collected by Dr. Wolberg (1990), and the results are evaluated for performance measure (detection rate and false alarm rate). Results show that our approach has promising performance on breast cancer diagnosis and great potential in the area of clinical diagnosis. Effects of parameters setting in the CSPRA are discussed, and the experimental results are compared with the previous works.