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In this paper we tried to loosen the following knot: at somepoint in mid toddlerhood, children evidence a gender/sex—a personal sense of group belonging which alsopredicts individual behaviors and wants—play, dress andpeer preferences. Yet at birth, although individual patterndifferences in embodied responses to new stimuli exist,these do not appear to correlate with gender/sex (Kagan,1994), and few measurable differences exist betweenfemale-designated and male-designated infants. As gender/sex becomes visible and measurable, it already seems toemanate from deep within the body. How does individual—apparently non-gender/sex-correlated-- variability turninto individual gender/sex identity? How do measurablegender/sex-related group differences appear? In this essaywe contrast process-based theories of gender/sex identityand development with theories that posit identity as aninherent trait. From our own research, we offer data thatdemonstrate differences in behavior and infant handlingin mother-son compared to mother-daughter dyads. Weuse these findings and others in the literature to develop atheory of embodiment and conclude with a proposalto refocus research in the field of infant gender/sexdevelopment. Specifically, we urge the use of longitudinal,multi-disciplinary research designs and analytical tools thatemphasize emerging properties and developmental process.

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This paper presents a genetic k-means algorithm for clustering high dimensional objects in subspaces. High dimensional data faces data sparsity problem. In this algorithm, we present the genetic k-means clustering process to calculate a weight for each dimension in each cluster and use the weight values to identify the subsets of important dimensions that categorize different clusters. This is achieved by including the weight entropy in the objective function that is minimized in the k-means clustering process. Further, the use of genetic algorithm ensure for converge to the global optimum. The experiments on UCI data has reported that this algorithm can generate better clustering results than other subspace clustering algorithms.